Serialized thinking problem solving system based on MCP protocol

Abstract

The invention discloses a serialized thinking problem solving system based on an MCP protocol. Semantic analysis is carried out on an input task, a thinking step sequence with a causal sequence is automatically constructed, and the content, the number and the deduction state of each step are recorded in a structured mode. A self-adaptive thinking regulation and control mechanism is arranged, the number of target steps can be dynamically adjusted according to task progress, and the steps can be inserted, deleted or rearranged. And meanwhile, dividing a plurality of thinking paths at a specified step is supported, management is carried out in a tree structure, and path convergence or selection is executed according to the path achievement degree. And the context integration module is used for uniformly storing the main path and the branch paths and generating a traceable context snapshot. The whole structured thinking process is externally output through the interaction module conforming to the MCP communication protocol so as to realize cross-system real-time synchronization and control. According to the method, a complex reasoning process can be presented in a structured, transparent and traceable mode, and the interpretability, stability and execution efficiency of a multi-step task are improved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and intelligent reasoning control technology, and in particular to a serialized thinking problem-solving system based on the MCP protocol. Background Technology

[0002] In recent years, large language models have been increasingly used in complex tasks such as consultation, planning, analysis, and decision-making. However, existing reasoning frameworks generally remain at the stage of "single-round generation" or "fixed-chain reasoning." The thought processes they generate often lack structured expression, are not traceable, and cannot dynamically adjust the depth of reasoning. When faced with multi-step problems, constantly changing conditions, or problems requiring repeated correction, the models are prone to phenomena such as skipping steps in the thought process, insufficient argumentation, and amplification of local errors, leading to a decrease in the overall reliability of the solution.

[0003] To improve performance on complex tasks, some technologies attempt to extend the reasoning path through pre-defined thought chains, cue engineering, or external tools. However, most of these methods employ static structures, failing to adjust the number of reasoning steps in real-time as the task changes, and lacking clear annotation of the logical relationships, causes, and changes of each step. Furthermore, existing methods handle reasoning history rather crudely, lacking systematic management of modification, replacement, and branching processes, and thus cannot support effective auditing and backtracking of the entire reasoning process.

[0004] Furthermore, complex problems often require parallel deduction of multiple hypothetical paths to explore different inference directions. However, traditional models can only proceed along a single path, and even when multi-path mechanisms are introduced, they lack clear branch identifiers, source numbers, and criteria for path merging. As a result, the system struggles to determine the merits of different inference routes or to perform path rollback when necessary. The inference process becomes fragmented and uncontrollable, limiting the model's ability to handle highly complex tasks.

[0005] Meanwhile, the inference steps of current models are typically in the form of unstructured text, lacking standardized data interfaces. This makes it difficult to integrate the inference process with external systems, achieve stable cross-system synchronization, and meet the needs of task management, audit trails, or engineered deployment.

[0006] In summary, existing technologies generally suffer from the following shortcomings: 1. The reasoning steps are not represented in a structured way, lacking clear sequential relationships and traceability; 2. The depth of reasoning cannot be dynamically adjusted according to the task, resulting in a rigid thought process; 3. The lack of a mechanism for revising thought processes makes it impossible to construct a clear history of thought evolution; 4. Multi-path reasoning lacks effective branch management, comparison, and merging mechanisms; 5. Lacking a unified protocol layer, the reasoning process is difficult to reliably interact with external systems.

[0007] These issues cause the model to exhibit instability and uninterpretability when handling complex tasks, limiting its controllability and security in engineering applications. Therefore, the industry urgently needs a serialized thought generation system capable of structured expression, dynamic control, fine-grained management of the reasoning process, and cross-system communication capabilities to improve the overall performance and reliability of complex tasks.

[0008] Therefore, existing technologies still need to be improved. Summary of the Invention

[0009] Given that existing models suffer from loose structure, lack of traceability, and difficulty in engineering integration in areas such as reasoning step management, depth of thought control, historical revision records, and multi-path reasoning, there is an urgent need for a technical solution that can explicitly serialize the thought process, support adaptive control, and possess branch management and standardized communication capabilities to improve the stability, transparency, and controllability of complex task processing.

[0010] The technical solution of the present invention is as follows: This invention provides a serialized thinking problem-solving system based on the MCP protocol. The system includes a thinking step generation module, a thinking control module, a thinking revision module, a thinking branching module, a context integration module, and a protocol communication module. The thought process generation module is used to perform semantic parsing on the input task and establish a sequence of thought processes with sequential dependencies, and to generate structured thought data for each thought process. The thought control module is used to adaptively adjust the target number of thought steps based on the task processing progress, and to insert, delete or rearrange the thought step sequence. The thought revision module is used to generate revision steps for any generated thought step and record the revision associations; The thought branching module is used to differentiate multiple independent thought paths at a specified thought step and assign a unique identifier to each path; The context integration module is used to uniformly store the structured thinking data of the main path and each branch path as a traceable context state; The protocol communication module is used to encapsulate the above-mentioned structured thinking data according to the MCP communication protocol and output or receive control information to external systems through available transmission methods to ensure cross-system synchronization of thinking states.

[0011] In one embodiment, the structured thinking data includes at least fields for representing step content, step number, target total number of steps, revision identifier, original step number, branch source step, branch identifier, and need for further deduction, so as to achieve explicit management of thinking sequence.

[0012] In one embodiment, the thinking control module automatically increases or decreases the total number of target steps based on indicators such as confidence assessment, subtask progress, external feedback, and resource consumption, and renumbers the unexecuted steps after adjustment to maintain sequence continuity.

[0013] In one embodiment, the thought revision module employs a multi-version retention mechanism, simultaneously saving the original steps and revision steps, and generating a revision trajectory in the context integration module, making the thought process comparable, auditable, and traceable.

[0014] In one embodiment, the thought branching module stores multiple thought paths in a tree structure, and merges, replaces or retains the paths based on the achievement of the path goal, reasoning consistency or external selection instructions, and records each path selection.

[0015] In one embodiment, the context integration module supports generating context snapshots by time point, step number, or branch identifier, and provides context rollback and difference comparison capabilities to enable historical tracing or path recovery during task execution.

[0016] In one embodiment, the protocol communication module supports encapsulating structured thought data in JSON format or compressed encoding format, and providing continuous output to external systems through standard input / output streams or event push streams to support real-time thought state synchronization.

[0017] In one embodiment, the system constructs an initial reasoning framework based on task type and generates an initial target number of steps by automatically judging the matching relationship between task complexity and reasoning depth, thereby providing differentiated sequential thinking strategies for different types of tasks.

[0018] In one embodiment, the system operates as a tool that can be invoked by a large language model, and provides a unified description of input parameters and output format, enabling the large language model to obtain structured thinking data through a standardized interface.

[0019] In one embodiment, the system adopts a distributed architecture, with each module exchanging data through an internal communication channel, and a handshake verification and retry mechanism is set in the protocol communication module to ensure stable connection and high-confidence synchronization with external systems.

[0020] In summary, this invention addresses the problems that large models often encounter in complex tasks, such as broken reasoning chains, uncontrollable paths, difficulty in adaptive depth, and lack of process recording. It proposes a complete sequential thinking generation system. From the structured representation of thinking steps to the dynamic control of reasoning depth, and further to the recording of revisions for arbitrary steps and the management of multi-path parallel thinking, this invention constructs a thinking framework that can realistically reflect the reasoning process and continuously evolve according to task changes. Simultaneously, through contextual unification and integration and the MCP protocol communication mechanism, this invention ensures that the reasoning process can be stably synchronized across different systems, making the thinking process not only interpretable and traceable but also capable of engineering implementation. This solution has significant advantages in improving the quality of decision-making in complex tasks, enhancing reasoning robustness, and improving system controllability, providing reliable support for intelligent reasoning and system collaboration in multiple scenarios.

[0021] Unlike existing technologies that generally rely on linear reasoning, fixed template logic, or single-path development, this invention establishes a system-level linkage mechanism across multiple dimensions, including structured expression of thought steps, dynamic control, revision tracing, multi-path parallelism, context snapshot maintenance, and protocol-based interaction. This enables the model's reasoning process to possess both controllability and interpretability for the first time. Traditional solutions often only generate "results" but cannot manage the reasoning chain itself. Consequently, if logical jumps, information omissions, embellished reasoning, or path confusion occur, the system cannot automatically correct them. In contrast, this invention maintains traceable structural information at each stage of reasoning, achieving self-correction and self-maintenance similar to expert-level thinking processes—an effect that traditional large-scale model reasoning frameworks struggle to achieve.

[0022] More importantly, this invention, through a multi-version revision mechanism and a path branching mechanism, endows the inference flow with "retrogression in the time dimension" and "branchability in the spatial dimension." There are no mature solutions for this capability in existing technologies. In this system, revisions to any step do not lead to contextual confusion, and expansions of any path do not cause logical conflicts, thus enabling the system to maintain the stability and consistency of the inference chain in highly complex decision-making tasks. Furthermore, this invention achieves real-time synchronization through the MCP protocol, allowing the thought structure to be directly consumed by external models, thereby enabling cross-system collaboration in the inference process. This module-level transparency is completely absent in traditional black-box inference frameworks.

[0023] In summary, the technical effects of this invention are not only reflected in the improvement of the quality of the reasoning results, but also in the ability to finely manage the reasoning process itself, enabling large-scale model reasoning to leap from a "generative" to a new paradigm of "structured, controllable, and interpretable", which has obvious unexpectedness. Attached Figure Description

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 The present invention provides a system structure block diagram of a serialized thinking problem-solving system based on the MCP protocol. Detailed Implementation

[0025] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The embodiments of the invention are described below in conjunction with the accompanying drawings.

[0026] One embodiment of the present invention provides a serialized thought problem-solving system based on the MCP protocol. Please refer to [link to relevant documentation]. Figure 1 The system includes a thought process generation module 1, a thought regulation module 2, a thought revision module 3, a thought branching module 4, a context integration module 5, and a protocol communication module 6. The thought process generation module 1 is used to perform semantic parsing on the input task and establish a sequence of thought processes with sequential dependencies, and to generate structured thought data for each thought process. First, this system aims to handle complex reasoning tasks and consists of multiple functional modules that operate collaboratively. Upon receiving task input, the system first performs semantic parsing on the task text using the thought step generation module 1, including identifying the task objective, reasoning constraints, and possible contextual dependencies. Based on the parsing results, this module generates an initial sequence of thought steps with a logical order. Each step forms structured thought data, including a number and a description of the step content that can be used for subsequent management. During operation, the thought step generation module 1 also adjusts the granularity of the initial steps appropriately based on external models or task status requirements, ensuring that the generated steps are sufficient to support a complete reasoning chain.

[0027] In a further embodiment, the thinking control module 2 is used to adaptively adjust the target number of thinking steps based on the task processing progress, and to insert, delete or rearrange the sequence of thinking steps. Specifically, after generating the initial sequence, the thought control module 2 takes over the depth and sequence control of the reasoning process. This module continuously monitors the progress of the task, such as whether key clues have appeared, whether there are any skips in the current thought chain, and whether the reasoning conclusion needs further support. When it is determined that the current reasoning depth is insufficient or the logic between steps is inconsistent, the thought control module 2 automatically inserts new steps to fill the reasoning gaps; if some steps are no longer necessary, they can be deleted. For changes in numbering caused by additions or deletions, the module will reorder the steps that have not yet been executed to ensure that the entire step chain remains consistent and uninterrupted. Through this mechanism, the system can dynamically adapt to the changing needs of the task during the reasoning process, rather than relying on a static link of fixed length.

[0028] In a further embodiment, the thought revision module 3 is used to generate revision steps for any generated thought step and record the revision association; Specifically, when a thought process step contains deviations, lacks information, or is no longer applicable to the current context, the thought revision module 3 is triggered. This module generates a new revised step for that step and records its relationship with the original step in the structured data. The revised step does not overwrite the original step; instead, it is stored as an update relationship to ensure the system can track the evolution of the reasoning history at any time. In this way, the system maintains an auditable reasoning trajectory, allowing external reviewers or model debugging to clearly see the modification process and the reasons for the modifications.

[0029] In a further embodiment, the thought branching module 4 is used to differentiate multiple independent thought paths at a specified thought step and assign a unique identifier to each path; Specifically, when a reasoning task has multiple possibilities, the assumptions are independent, or multiple paths need to be evaluated in parallel, the thought branching module 4 is used to differentiate independent reasoning paths from specified thought steps. This module assigns a unique identifier to each path, enabling different paths to maintain independent logical links in subsequent steps. The generation, regulation, and revision of steps within each branch path can all be performed independently, allowing the system to explore multiple reasoning directions within the same task. In the later aggregation stage, the system can retain, replace, or merge these branches based on task completion, path rationality, or external instructions.

[0030] In a further embodiment, the context integration module 5 is used to uniformly store the structured thinking data of the main path and each branch path as a traceable context state. More specifically, to manage the ever-increasing number of thought steps and branch paths, the context integration module 5 aggregates all thought data generated by the main path and each branch path, forming a traceable and reconstructable context state. This module maintains context records with temporal order and step-by-step relationships, allowing the system to restore the reasoning state to a specific step or branch at any time. Furthermore, the context integration module 5 supports comparing multiple versions of steps, providing a basis for the system to analyze changes in reasoning or perform rollback operations.

[0031] In a further embodiment, the protocol communication module 6 is used to encapsulate the above-mentioned structured thinking data according to the MCP communication protocol and output or receive control information to external systems through available transmission methods to ensure cross-system synchronization of thinking states.

[0032] More specifically, when the system interacts with an external large language model or control system, the protocol communication module 6 is responsible for encapsulating the generated structured thinking data. This module organizes the content, number, version, and other data of each step according to the MCP protocol specification and transmits it to the external environment through the transmission methods supported by the system. For example, during continuous reasoning, when the system generates new steps, adjusts the depth of thinking, or generates branches, the protocol communication module 6 pushes updated information to the external model in real time, ensuring that the external system can participate in reasoning or provide decision feedback in the latest state. To ensure communication stability, this module is also configured with handshake verification and failure retry mechanisms, thus maintaining reliable data synchronization even under high load or network fluctuations.

[0033] Through the synergistic effect of the above modules, this system can generate reasoning links for complex tasks in a structured, traceable, and dynamically controllable manner, making the reasoning process both flexible and manageable, and meeting the actual needs of intelligent reasoning tasks in multiple scenarios.

[0034] In a further embodiment, the structured thinking data includes at least fields for representing step content, step number, target total number of steps, revision identifier, original step number, branch source step, branch identifier, and need for further deduction, so as to achieve explicit management of the thinking sequence.

[0035] Specifically, in this embodiment, structured thinking data is stored in a unified data description format for reliable transmission of reasoning state between different modules. This data structure includes at least a text field representing the content of each step, a number field indicating the sequence of steps, and a target total number of steps representing the overall system reasoning plan. When generating a revised step, the system records a revision identifier in the data structure and explicitly marks the historical step corresponding to the revision through the original step number field, enabling the system to accurately locate the revision relationship during subsequent debugging or tracing. For tasks with multi-path reasoning requirements, the system further records the branch source steps and path identifiers in the structure, ensuring that each path maintains an independent and clear logical link. This also facilitates the context integration module 5 in comparing, merging, or rolling back different paths in subsequent stages. The "Continue Reasoning Requirement" field informs the external model or controller during the reasoning process whether the current thinking state needs further expansion. This is particularly important in highly complex tasks, effectively preventing the reasoning chain from being interrupted or overextended. Through the collaborative expression of the above fields, the structuring level of the entire reasoning process is significantly improved, enabling the system to accurately recover and maintain a complete reasoning sequence at any stage of execution.

[0036] In a further embodiment, the thinking control module 2 automatically increases or decreases the total number of target steps based on indicators such as confidence assessment, subtask progress, external feedback, and resource consumption, and renumbers the unexecuted steps after adjustment to maintain sequence continuity.

[0037] Specifically, in this embodiment, the dynamic management of reasoning depth by the thinking control module 2 is based on a comprehensive judgment of multi-dimensional signals, including the confidence index within the system, the completion status of sub-tasks, and feedback information provided by external devices. When logical jumps, insufficient arguments, or information chain breaks occur during the reasoning process, the module automatically adds new steps to make up for the missing parts, keeping the reasoning chain coherent; when some steps are redundant or have been covered by subsequent information, the module removes or merges them. After performing the above adjustments, to prevent the sequence number from being disordered and causing the context to be unable to be effectively linked, the system will renumber the steps that have not yet been executed, while ensuring that the number of the executed parts is consistent with the historical version number for subsequent tracing. The module will also dynamically adjust the step growth rate according to the task resource situation (including computing resources, latency constraints, etc.) to avoid the reasoning chain growing too fast and causing abnormal system load. Through the above adaptive mechanism, the system can intelligently manage the reasoning depth according to the task characteristics, so that the reasoning sequence is neither over-expanded nor logically broken due to insufficient steps.

[0038] In a further embodiment, the thought revision module 3 adopts a multi-version retention mechanism, which saves both the original steps and the revision steps, and generates a revision trajectory in the context integration module 5, so that the thought process is comparable, auditable and traceable.

[0039] Specifically, in this embodiment, the thought revision module 3 employs a multi-version history retention mechanism to manage each revised thought step. When the system detects that a step is inconsistent with the new context, its logical expression is insufficient, or it has been overturned by subsequent information, it creates a corresponding revised step and retains the original step number in the structured data to indicate the evolutionary relationship between the two. The original step is not completely overwritten but is retained in the context record as an older version, allowing the system to view the original inference path and its modification process during subsequent analysis, debugging, or review. After receiving the revised step, the context integration module 5 automatically updates the reference relationships between steps to ensure that the revised inference flow is consistent with the current context. If necessary, the system can also provide a content comparison between the original and revised versions to determine the impact of the revision on the overall inference chain. This multi-version retention method makes the inference process transparent and interpretable, and provides a data foundation for subsequent path analysis, model behavior analysis, or audit trails.

[0040] In a further embodiment, the thinking branch module 4 stores multiple thinking paths in a tree structure, and merges, replaces or retains the paths based on the achievement degree of the path goal, reasoning consistency or external selection instructions, and records each path selection.

[0041] More specifically, in a multi-path reasoning task application scenario, the system uses the thought branching module 4 to differentiate multiple reasoning paths at specified steps to explore different hypotheses or decision directions. Assuming the task faces two possibilities with opposing premises at a certain stage, the system can create two independent paths based on this key step, each with its own independent step sequence and structured thought record. The branch paths are maintained in a tree structure, including path hierarchy, source steps, and path identifiers, clearly presenting the structural relationship of the reasoning tree. As reasoning deepens, different paths may produce different conclusions, confidence levels, or logical consistency evaluations. The system selects the reasoning chain that ultimately becomes the main path based on these indicators, while other paths are retained, merged, or discarded according to task requirements. During path merging, the system records the merging basis and results to ensure the overall reasoning process can be reconstructed and audited. This branch management method enables the system to explore multi-directional reasoning in parallel in complex tasks, improving the comprehensiveness of reasoning and the reliability of results.

[0042] In a further embodiment, the context integration module 5 supports generating context snapshots by time point, step number, or branch identifier, and provides context rollback and difference comparison capabilities to enable historical tracking or path recovery during task execution.

[0043] Specifically, in this embodiment, the context integration module 5 periodically generates traceable context snapshots during the inference process. Based on task triggering conditions (e.g., completing key steps, generating branches, generating revision steps, or receiving external synchronization instructions), the system packages the structured thinking data of the current main path and all branch paths, identifying them with version numbers or timestamps. Each snapshot contains a sequence of steps, the relationships between steps, the path structure, and the revised version of each step, enabling a complete description of the inference state at a given moment. When the system experiences inference deviations, path conflicts, or when the user needs to review historical versions, they can restore to a specific snapshot by selecting the corresponding time point, step number, or path identifier. The context integration module 5 also provides a snapshot comparison function, allowing the system to clearly present differences between snapshots, including added steps, step rearrangements, path changes, or revision records, thus facilitating debugging, auditing, and task tracking. In engineering applications, this snapshot mechanism ensures that even in complex inference scenarios, the system maintains a clear and recoverable inference history.

[0044] In a further embodiment, the protocol communication module 6 supports encapsulating structured thought data in JSON format or compressed encoding format, and provides continuous output to external systems through standard input / output streams or event push streams to support real-time thought state synchronization.

[0045] In one specific implementation, the protocol communication module 6 is responsible for encapsulating structured reasoning data according to the MCP protocol format to ensure stable communication between the system and external models or controllers. The system organizes the content, number, path information, revision records, and inference status of each step into structured messages and outputs them via JSON encoding or compressed encoding. For scenarios requiring real-time push of inference updates, the system sends incremental changes to external systems via event push streams (e.g., SSE), ensuring synchronization at every stage of inference progression. For tasks with strong consistency requirements, the system uses standard input / output streams for point-to-point data exchange and combines this with a handshake verification mechanism to ensure consistency in data format, version number, and status. In high-throughput or complex scenarios, the communication module caches and batches the pushed content to reduce network pressure and improve stability. Through this communication mechanism, the system can synchronize status in real time at important nodes such as inference generation, revision, branching, and context switching, meeting the engineering requirements of highly collaborative inference.

[0046] In a further embodiment, the system constructs an initial reasoning framework based on task type, and generates an initial target number of steps by automatically judging the matching relationship between task complexity and reasoning depth, thereby providing differentiated sequential thinking strategies for different types of tasks.

[0047] In this embodiment, upon receiving task input, the system automatically constructs an initial reasoning framework based on the task type. The system analyzes the structure, information density, logical complexity, and expected output format of the task objective to determine the required depth of reasoning and granularity of steps. For example, for planning tasks, the system tends to generate initial steps in logical stages such as objective decomposition, condition constraints, path analysis, and risk assessment; for argumentation tasks, the system constructs an initial framework of premise identification, evidence association, logical deduction, and conclusion generation. During execution, the thinking control module 2 dynamically adjusts this framework based on real-time task feedback to ensure the number of initial steps matches the task complexity. In this way, the system can automatically generate reasoning strategies based on the actual needs of the task, rather than relying on fixed templates, thereby ensuring the rationality of the reasoning chain and execution efficiency under different task types.

[0048] In a further embodiment, the system operates as a tool that can be invoked by a large language model, and provides a unified description of input parameters and output format, enabling the large language model to obtain structured thinking data through a standardized interface.

[0049] In an engineered deployment scenario, this system operates as a tool that can be invoked by a large language model. By defining unified input parameter constraints, including task description, control preferences, inference depth requirements, and external control signals, the system ensures that external models can provide task configurations consistently when calling the tool. The system returns content in a structured format, including step sequences, path states, revision information, and a final inference snapshot, making it easy for external models to parse and utilize the results. External models can drive the system to generate the next step, request path switching, or trigger revisions through multiple rounds of calls, thereby achieving a model-driven, controllable inference chain. This tool-based approach endows large language models with "structured inference capabilities," avoiding the common problems of discontinuous inference, information loss, or inability to backtrack in single-round generation, and enabling the system to run stably and collaboratively within a hybrid inference framework.

[0050] In a further embodiment, the system adopts a distributed architecture, with each module exchanging data through an internal communication channel, and a handshake verification and retry mechanism is set in the protocol communication module 6 to ensure stable connection and high-confidence synchronization with external systems.

[0051] In another embodiment, the system employs a distributed architecture, with each module running as an independent service component and transmitting structured thought data through a lightweight internal communication channel. For example, the thought step generation module 1 can be deployed as a dedicated reasoning and parsing service, the thought regulation module 2 and revision unit can serve as reasoning scheduling services, and the context integration module 5 can operate as a state management service. The protocol communication module 6 is deployed at the system edge as an external interface layer, responsible for handling all interaction requests with external systems. To ensure stable operation under high load or network fluctuations, the communication module incorporates handshake verification, timeout retry, and breakpoint recovery mechanisms, enabling the system to automatically recover synchronization in abnormal states. Through this distributed deployment approach, the system maintains stable performance and provides consistent reasoning behavior under large-scale task processing, cross-system collaboration, and different computing environments.

[0052] In summary, this invention, through the collaborative design of multiple modules including structured thinking data, thinking control, step revision, path branching, context snapshots, and protocol communication, enables large models to possess controllable, traceable, and interpretable reasoning capabilities when performing complex tasks. The entire system, based on structured data representation, with dynamic control as its core and context consistency maintenance as a constraint, realizes a complete reasoning chain from step generation, deep management, logical revision to path evolution. Regardless of the complexity of the task itself, its unfolding method, or the source of input, the system can organize the reasoning process within a stable framework, ensuring that the reasoning chain has clear hierarchy, reliable sequential relationships, and historical records available for subsequent analysis or verification.

[0053] In practical applications, the reasoning mechanism provided by this invention effectively solves common problems in traditional large-scale model reasoning, such as "missing steps, confused logic, inability to trace origins, and path jumps," making the execution of complex tasks closer to the thinking rhythm of human experts. Simultaneously, through the communication structure of the MCP protocol, this system can efficiently collaborate with external models, ensuring real-time synchronization of reasoning content during generation, revision, and branching. Furthermore, with the help of a distributed architecture, each module can be independently expanded while forming a closed loop through a unified interface, providing engineering feasibility for large-scale reasoning tasks deployed on different platforms and in different computing environments.

[0054] Overall, this invention is not limited to a specific type of task, but rather clarifies and controls complex logical processes through a unified structured reasoning framework, laying the foundation for future multi-model collaborative and hybrid reasoning systems. Its technical effect lies in significantly improving the reliability and transparency of the reasoning process, enabling large models to execute critical decisions or highly complex tasks with greater interpretability, demonstrating technical advantages that are difficult to achieve with traditional solutions.

[0055] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A serialized thinking problem-solving system based on the MCP protocol, characterized in that, The system includes a thought process generation module, a thought regulation module, a thought revision module, a thought branching module, a context integration module, and a protocol communication module. The thought process generation module is used to perform semantic parsing on the input task and establish a sequence of thought processes with sequential dependencies, and to generate structured thought data for each thought process. The thought control module is used to adaptively adjust the target number of thought steps based on the task processing progress, and to insert, delete or rearrange the thought step sequence. The thought revision module is used to generate revision steps for any generated thought step and record the revision associations; The thought branching module is used to differentiate multiple independent thought paths at a specified thought step and assign a unique identifier to each path; The context integration module is used to uniformly store the structured thinking data of the main path and each branch path as a traceable context state; The protocol communication module is used to encapsulate the above-mentioned structured thinking data according to the MCP communication protocol and output or receive control information to external systems through available transmission methods to ensure cross-system synchronization of thinking states.

2. The sequential thinking problem-solving system according to claim 1, characterized in that, The structured thinking data includes at least fields for representing step content, step number, total number of target steps, revision identifier, original step number, branch source step, branch identifier, and requirements for further deduction, so as to achieve explicit management of thinking sequences.

3. The sequential thinking problem-solving system according to claim 1, characterized in that, The thought control module automatically increases or decreases the total number of steps based on indicators such as confidence assessment, subtask progress, external feedback, and resource consumption. After adjustment, it renumbers the unexecuted steps to maintain sequence continuity.

4. The sequential thinking problem-solving system according to claim 1, characterized in that, The thought revision module adopts a multi-version retention mechanism, which saves both the original steps and the revision steps, and generates a revision trajectory in the context integration module, making the thought process comparable, auditable and traceable.

5. The sequential thinking problem-solving system according to claim 1, characterized in that, The thought branch module stores multiple thought paths in a tree structure, and merges, replaces or retains the paths based on the achievement of the path goal, reasoning consistency or external selection instructions, and records each path selection.

6. The sequential thinking problem-solving system according to claim 1, characterized in that, The context integration module supports generating context snapshots by time point, step number, or branch identifier, and provides context rollback and difference comparison capabilities to enable historical tracking or path recovery during task execution.

7. The sequential thinking problem-solving system according to claim 1, characterized in that, The protocol communication module supports encapsulating structured thinking data in JSON format or compressed encoding format, and provides continuous output to external systems through standard input / output streams or event push streams to support real-time thinking state synchronization.

8. The sequential thinking problem-solving system according to claim 1, characterized in that, The system constructs an initial reasoning framework based on task type, and generates an initial target number of steps by automatically judging the matching relationship between task complexity and reasoning depth, thereby providing differentiated sequential thinking strategies for different types of tasks.

9. The sequential thinking problem-solving system according to claim 1, characterized in that, The system operates as a tool that can be invoked by large language models, and provides a unified description of input parameters and output format, enabling large language models to obtain structured thinking data through standardized interfaces.

10. The sequential thinking problem-solving system according to claim 1, characterized in that, The system adopts a distributed architecture, with each module exchanging data through an internal communication channel. A handshake verification and retry mechanism is set in the protocol communication module to ensure stable connection and high-confidence synchronization with external systems.

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