Method for realizing agent development MCP service through computing power of intelligent computing center

Abstract

The invention provides a method for realizing agent development MCP service through computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, determining interface information of a target application programming interface API, the target API being an interface of a target platform; s2, generating a first coding task based on the interface information through an intelligent agent of the intelligent computing center; s3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; and S4, registering the first MCP service component to the intelligent computing center. According to the invention, the development efficiency of the MCP service of the intelligent computing center can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of intelligent computing centers, intelligent computing centers, and computing power infrastructure, and particularly relates to a method for implementing an MCP service for intelligent agent development through the computing power of an intelligent computing center. Background Art

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have emerged as the times require.

[0003] An "intelligent computing center" refers to a facility that provides the required computing power, data, and algorithms for artificial intelligence applications (such as scenarios for artificial intelligence deep learning model development, model training, and model inference) by using large-scale heterogeneous computing power resources, including general computing power and intelligent computing power. An intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0004] The "intelligent computing center" includes, but is not limited to, the "intelligent computing center".

[0005] An "intelligent computing center", that is, an artificial intelligence computing center, is a type of computing power infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications based on artificial intelligence theory and using an artificial intelligence computing architecture.

[0006] "Computing power" is the core of "intelligent computing centers" and "intelligent computing centers". It is the ability of computer devices or computing / data centers to process parameters, the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement, the computing ability to process parameter data and achieve the output of target results, and a new type of productive force integrating parameter computing power, network carrying capacity, and data storage capacity. It mainly provides services to society through computing power infrastructure.

[0007] An intelligent agent is an important component of the overall service ecosystem of an intelligent computing center, mainly responsible for directly interacting with users and providing service support relying on the computing power resources of the intelligent computing center. In the prior art, the scope of capabilities and business boundaries of an intelligent agent are extended through a Model Context Protocol (MCP) service (Server), and the access to data on a third-party platform is achieved by encapsulating an Application Programming Interface (API) through the MCP Server. However, in the prior art, the MCP Server is mainly developed and registered through manual programming. In the case of complex configuration of the MCP Server, a large amount of time is required for programming, resulting in very low development efficiency of the MCP Server of the intelligent computing center.

[0008] It can be seen that there is a problem of very low development efficiency of the MCP Server in the intelligent computing center in the prior art. Summary of the Invention

[0009] An embodiment of the present invention provides a method for implementing an MCP service for intelligent agent development through the computing power of an intelligent computing center, so as to solve the problem of very low development efficiency of the MCP Server in the intelligent computing center in the prior art.

[0010] To solve the above problems, the present invention is implemented as follows: In a first aspect, an embodiment of the present invention provides a method for implementing an MCP service for intelligent agent development through the computing power of an intelligent computing center, including: Step S1, determining interface information of a target application programming interface API, where the target API is an interface of a target platform; Step S2, generating a first coding task based on the interface information through an intelligent agent in the intelligent computing center; Step S3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; Step S4, registering the first MCP service component to the intelligent computing center.

[0011] In one embodiment, step S1 includes: Step S11, receiving requirement information sent by a user, where the requirement information includes target requirement data; Step S12, querying based on the target requirement data to determine the target platform including the target requirement data; Step S13, obtaining interface information of the target API of the target platform.

[0012] In one embodiment, step S1 includes: Step S1', receiving the interface information of the target API sent by the target platform; After step S4, the method further includes: Step S5, sending the first MCP service component to the target platform.

[0013] In one embodiment, step S2 includes: Step S21, obtaining an MCP protocol template and a preset prompt word through the intelligent agent, where the MCP protocol template is obtained by the intelligent agent parsing MCP; Step S22, calling a large language model through the intelligent agent, and generating the first coding task based on the preset prompt word, the interface information, and the MCP protocol template.

[0014] In one embodiment, step S4 includes: Step S41: Send a first acquisition request to the target platform through the first MCP service component, where the first acquisition request is used to acquire first data; Step S42: When receiving the first data sent by the target platform, register the first MCP service component to the intelligent computing center.

[0015] In one embodiment, step S4 further includes: Step S43: When not receiving the first data sent by the target platform, use the agent to correct the first encoding task based on the interface information to obtain a second encoding task; Step S44: Use the agent to generate a second MCP service component corresponding to the second encoding task; Step S45: Send a second acquisition request to the target platform through the second MCP service component, where the second acquisition request is used to acquire first data; Step S46: When receiving the first data sent by the target platform, register the second MCP service component to the intelligent computing center.

[0016] In one embodiment, the method further includes: Step S6: Receive demand adjustment information sent by a user, where the demand adjustment information is used to represent that the user's demand is adjusted from the first data of the target platform to the second data of the target platform; Step S7: Use the above-mentioned agent to correct the first encoding task based on the demand adjustment information to obtain a third encoding task; Step S8: Use the agent to generate a third MCP service component corresponding to the third encoding task; Step S9: Send a third acquisition request to the target platform through the third MCP service component, where the third acquisition request is used to acquire the second data; Step S10: When receiving the second data sent by the target platform, register the third MCP service component to the intelligent computing center.

[0017] In the present invention, the interface information of a target application programming interface (API) is determined, where the target API is an interface of a target platform; through an intelligent agent of the intelligent computing center, a first encoding task is generated based on the interface information; through the intelligent agent, a first MCP service component corresponding to the first encoding task is generated; and the first MCP service component is registered with the intelligent computing center. In this way, through the intelligent agent of the intelligent computing center, the encoding and generation of the first MCP service component are realized, achieving the automatic development of the first MCP service component, and greatly improving the development efficiency compared with the manual development of the MCP service component. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 is a flowchart of a method for developing an MCP service by an intelligent agent through the computing power of an intelligent computing center provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of an intelligent computing center provided by an embodiment of the present invention; Figure 3 is a schematic diagram of manually writing code provided by an embodiment of the present invention; Figure 4 is a schematic flowchart of generating a first MCP service component provided by an embodiment of the present invention; Figure 5 is an interaction schematic diagram of an intelligent agent in an intelligent computing center provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] The "computing power" described in the present invention refers to: the ability of a computer device or a computing / data center to process information, the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement, the computing ability to process information data and output a target result, a new type of productive force integrating information computing power, network carrying capacity, and data storage capacity, and mainly providing services to society through computing power infrastructure.

[0022] The "Computational Power" (CP) described in the present invention refers to: the ability of a data center server to process data and output results, which is a comprehensive indicator for measuring the computing power of a data center and includes general computing power, supercomputing power, and intelligent computing power. The commonly used measurement unit is the number of floating-point operations per second (FLOPS, 1 EFLOPS = 10^18 FLOPS). The larger the value, the stronger the comprehensive computing power. It is estimated that 1 EFLOPS is approximately the computing power output of 5 Tianhe-2A computers, or 500,000 mainstream server CPUs, or 2 million mainstream laptops. The calculation formula is: CP = CP 通用 + CP 智能 + CP 超级 。

[0023] The "Network Power" (NP) described in the present invention refers to: the performance of the data transmission capacity of computing power facilities, which is a comprehensive ability including network architecture, network bandwidth, transmission delay, intelligent management and scheduling, etc., and involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling ability.

[0024] The "Storage Power" (SP) described in the present invention refers to: the comprehensive ability of a data center in four aspects: data storage capacity, performance, security and reliability, and green and low-carbon, which is a comprehensive indicator for measuring the data storage capacity of a data center and includes external storage devices such as storage arrays and server internal storage devices. The commonly used measurement unit for storage capacity is exabyte (EB, 1 EB = 2^60 bytes), and the commonly used measurement unit for performance is the number of read and write operations per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB). The disaster recovery ratio is an important manifestation of security and reliability.

[0025] The "computing power infrastructure" described in the present invention refers to: a new type of information infrastructure that integrates information computing power, network carrying power, and data storage power, and can realize the centralized computing, storage, transmission, and application of information.

[0026] The "new type of information infrastructure" described in the present invention mainly includes network infrastructures such as 5G networks, fiber broadband networks, backbone networks, international communication networks, and satellite Internet, computing power infrastructures such as data centers, general computing power centers, intelligent computing centers, and supercomputing centers, and new technology infrastructures such as artificial intelligence, blockchain, and quantum computing.

[0027] The "computing power" described in the present invention includes: general computing power, intelligent computing power, and supercomputing power.

[0028] The "general computing power" described in the present invention refers to the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0029] The "intelligent computing power" described in the present invention refers to a computing platform that is deployed on a large scale for various artificial intelligence innovation applications based on dedicated chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit), such as natural language processing, machine vision, and so on.

[0030] The "super computing power" described in the present invention mainly refers to the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and processes extremely complex or data-intensive problems through a dedicated operating system. It is mainly used for computing in cutting-edge scientific fields, such as planetary simulation, drug molecule design, gene analysis, etc.

[0031] The "intelligent computing center" described in the present invention refers to a facility that provides the required computing power, data, and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training, and model inference scenarios) by using large-scale heterogeneous computing power resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center covers facilities, hardware, and software and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0032] The "intelligent computing center" described in the present invention includes but is not limited to the "intelligent computing center".

[0033] The "intelligent computing center" described in the present invention, that is, the artificial intelligence computing center, is a type of computing power infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications based on artificial intelligence theory and using an artificial intelligence computing architecture.

[0034] The "computing power center" described in the present invention refers to a facility mainly composed of infrastructure such as wind, fire, water, and electricity and IT software and hardware devices, which has computing power, transportation power, and storage power, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0035] The "supercomputing center" described in the present invention, that is, the supercomputing data center, is a data center based on supercomputers or large-scale computing clusters, which can provide functions such as large-scale computing, storage, and network services, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling, and genome sequencing.

[0036] The "computing power resources" described in the present invention refer to: technologies and facilities with information computing, transmission, storage, and application capabilities required for the development of the digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and support and guarantee resources such as wind, fire, water, and electricity.

[0037] The "models" described in the present invention include but are not limited to "large language models" and "multimodal large models".

[0038] The "large language model" described in the present invention refers to a large language model (LLM), which is a language model with a relatively large number of parameters, aiming to understand and generate human language, trained with a large amount of text data, and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0039] The "Multimodal Large Models" described in the present invention refer to: models trained by combining multimodal information such as text, images, videos, and audio, including but not limited to multimodal large language models.

[0040] The "agent" described in the present invention refers to an agent that can perceive the environment and take actions to achieve specific goals. It can be software, hardware, or a system, with autonomy, adaptability, and interaction capabilities. The agent perceives changes in the environment (such as through sensors or data input), makes judgments and decisions based on the knowledge and algorithms it has learned, and then executes actions to affect the environment or achieve a predetermined goal. Agents are widely used in the field of artificial intelligence, commonly found in automation systems, robots, virtual assistants, and game characters, and their core lies in the ability to learn independently and evolve continuously to better complete tasks and adapt to complex environments.

[0041] The "MCP service" described in the present invention refers to the Model Context Protocol (MCP) service (Server).

[0042] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for realizing the MCP service for agent development through the computing power of an intelligent computing center provided by an embodiment of the present invention. As Figure 1 shown, it includes the following steps: Step S1: Determine the interface information of the target Application Programming Interface (API), where the target API is the interface of the target platform.

[0043] The above target platform is the platform for the MCP service component to be developed, and the above target API is the interface of the target platform. Through the target API, data of the target platform can be obtained. The above interface information is the interface information of the target API. Through the interface information of the target API, encapsulation of the target API can be achieved, or the MCP service component that calls the target API can be encoded.

[0044] Step S2: Through the agent of the intelligent computing center, generate a first coding task based on the interface information.

[0045] The above agent is the agent of the intelligent computing center, which is used to play different roles and execute different tasks. In the present invention, the agent of the intelligent computing center is used to play the role of a programmer and perform encoding based on the above interface information to obtain a first coding task.

[0046] Among them, the structure of the intelligent computing center is as Figure 2 shown, including an environment layer, an agent network layer, a basic service layer, and a basic model layer. The environment layer includes information such as user interfaces, external APIs, web pages, documents, and devices; the agent network layer includes different types of agents, such as a main agent and agents in a domain expert agent pool. The agents in the domain expert agent pool include other auxiliary agents, planning agents, development agents, and deployment agents, etc., to achieve different functions through different functions; the basic service layer includes a development and debugging module, an effect evaluation module, a deployment monitoring module, and an MCP service component pool. The MCP service component pool includes a tool service module, a prompt word management module, a resource data module, etc. The code writing, generation, and testing of the MCP service component are achieved through different modules included in the basic service layer; the basic model layer includes different basic models, such as a large language model (LLM) and other models. The basic model layer can provide model functions for different agents, enabling different agents to achieve different functions.

[0047] It should be noted that in the prior art, the user needs to write code in the input area of the interface as Figure 3 shown to obtain a coding task, and then use an agent to perform encoding to generate the MCP service component corresponding to the coding task. In the present invention, the agent is pre-configured with a code writing function and automatically executes code writing by scheduling the computing power resources of the intelligent computing center, without the need for manual participation, and can effectively improve the execution efficiency.

[0048] Step S3: Through the agent, generate a first MCP service component corresponding to the first coding task.

[0049] It should be noted that the above intelligent agent is used to execute the function of writing and encapsulating the first coding task. By writing code for the first coding task, specific code is obtained, and then the intelligent agent encapsulates it to obtain the first MCP service component, so that other users can obtain data of the target platform through the encapsulated first MCP service component.

[0050] Step S4: Register the first MCP service component to the intelligent computing center.

[0051] By registering the first MCP service component to the intelligent computing center, the intelligent computing center can publish the first MCP service component, and the published first MCP service component can be called by different users, so as to obtain data of the target platform through the first MCP service component.

[0052] In the present invention, the interface information of the target application programming interface API is determined, and the target API is the interface of the target platform; through the intelligent agent of the intelligent computing center, a first coding task is generated based on the interface information; through the intelligent agent, a first MCP service component corresponding to the first coding task is generated; the first MCP service component is registered to the intelligent computing center. In this way, through the intelligent agent of the intelligent computing center, the coding and generation of the first MCP service component are realized, the automatic development of the first MCP service component is realized, and the development efficiency is greatly improved compared with the manual development of the MCP service component.

[0053] In one embodiment, step S1 includes: Step S11: Receive the requirement information sent by the user, and the requirement information includes target requirement data; Step S12: Query based on the target requirement data to determine the target platform including the target requirement data; Step S13: Obtain the interface information of the target API of the target platform.

[0054] It should be noted that the interface information of the target API can be determined through the requirement information sent by the user, that is, the user submits requirement information to the intelligent computing center, and the intelligent computing center then creates the first MCP service component according to the user's requirements.

[0055] Specifically, as Figure 4 shown, the requirement information sent by the user is to query weather information. At this time, the intelligent agent calls the research tool in the basic service layer to determine the target platform corresponding to the user's requirement information for querying weather, and obtain the interface information of the target API of the target platform; generate a first coding task corresponding to the interface information through the intelligent agent; call the development tool to generate a first MCP service component corresponding to the first coding task, and perform registration and testing.

[0056] In the present invention, demand information sent by a user is received, and the demand information includes target demand data; a query is made based on the target demand data to determine the target platform including the target demand data; interface information of the target API of the target platform is obtained. In this way, the interface information of the target API is determined through the user's demand information, and then the first MCP service component is generated, without the need for the user to confirm the interface information of the API, reducing the user's usage threshold and improving the usage effect.

[0057] In some embodiments, before the step S11, the method further includes: Step S10: Send a demand template to the user, where the demand template includes at least one input instruction, enabling the user to generate demand information based on the input instructions in the demand template.

[0058] In the present invention, by sending a demand template to the user to indicate the information that the user needs to input, the intelligent agent can determine the corresponding target platform according to the demand information sent by the user.

[0059] In one embodiment, the step S1 includes: Step S1': Receive the interface information of the target API sent by the target platform; After the step S4, the method further includes: Step S5: Send the first MCP service component to the target platform.

[0060] It should be noted that in addition to generating MCP service components according to the user's demands, the intelligent computing center can also develop MCP service components for third-party platforms to reduce the difficulty of third-party development of MCP service components.

[0061] Specifically, when the target platform needs to develop an MCP service component, the target platform sends the interface information of the target API to the intelligent computing center, and the intelligent computing center develops the first MCP service component through the intelligent agent according to the interface information of the target API; after the first MCP service component is developed, the first MCP service component is sent to the target platform, enabling the target platform to provide data for more users according to the first MCP service component.

[0062] In one embodiment, the step S2 includes: Step S21: Through the intelligent agent, obtain an MCP protocol template and a preset prompt word, where the MCP protocol template is obtained by the intelligent agent parsing MCP; Step S22: The agent invokes the large language model and generates the first encoding task based on the preset prompt words, the interface information, and the MCP protocol template.

[0063] In the present invention, through the agent, the MCP protocol template and the preset prompt words are obtained. The MCP protocol template is obtained by the agent's parsing of MCP. The agent invokes the large language model and generates the first encoding task based on the preset prompt words, the interface information, and the MCP protocol template. In this way, by invoking the large language model with the preset prompt words, the agent acts as a programmer, and the first encoding task is generated through the interface information and the MCP protocol template.

[0064] Specifically, as Figure 5 shown, the agent invokes the prompt word management module and the resource data module in the basic service to obtain the preset prompt words and the MCP protocol template respectively, and then generates the first encoding task through the agent.

[0065] In one embodiment, step S4 includes: Step S41: Send a first acquisition request to the target platform through the first MCP service component. The first acquisition request is used to acquire first data. Step S42: When receiving the first data sent by the target platform, register the first MCP service component to the intelligent computing center.

[0066] It should be noted that after deleting the first MCP service component, it is necessary to determine whether the first MCP service component can normally obtain the data of the target platform. Only when the data of the target platform can be normally obtained, it is registered and published by the intelligent computing center, so as to avoid the situation that the data that cannot be obtained is published, so that users can use the normally working MCP service component.

[0067] In the present invention, a first acquisition request is sent to the target platform through the first MCP service component. The first acquisition request is used to acquire first data. When receiving the first data sent by the target platform, the first MCP service component is registered to the intelligent computing center. In this way, through testing, it is ensured that the MCP service components in the intelligent computing center are all service components that can be normally used.

[0068] Specifically, as Figure 5 shown, after generating the first MCP service component, the agent invokes the effect evaluation module in the basic service layer, and the effect evaluation module sends a first acquisition request to the target platform and receives the first data sent by the target platform to determine whether the first MCP service component can work normally.

[0069] In one embodiment, step S4 further includes: Step S43: In the case where the first data sent by the target platform is not received, the agent corrects the first encoding task based on the interface information to obtain a second encoding task; Step S44: The agent generates a second MCP service component corresponding to the second encoding task; Step S45: A second acquisition request is sent to the target platform through the second MCP service component, and the second acquisition request is used to acquire the first data; Step S46: In the case where the first data sent by the target platform is received, the second MCP service component is registered in the intelligent computing center.

[0070] It should be noted that in the case where the first data sent by the target platform is not received, it is confirmed that the first MCP service component cannot achieve the expected function, and the first MCP service component needs to be corrected until the first data of the target platform can be acquired through the MCP service component.

[0071] Specifically, in the case where the first data sent by the target platform is not received, the agent corrects the first encoding task based on the interface information to obtain a second encoding task; the agent generates a second MCP service component corresponding to the second encoding task; a second acquisition request is sent to the target platform through the second MCP service component, and the second acquisition request is used to acquire the first data; in the case where the first data sent by the target platform is received, the second MCP service component is registered in the intelligent computing center.

[0072] In this way, the first encoding task is corrected through the interface information, and then the second MCP service component of the corrected second encoding task is generated until the second MCP service component can acquire the first data of the target platform, determining that the second MCP service component reaches the expected function (acquiring the first data), and then registering and publishing the second MCP service component, so that users can use the normally working MCP service component.

[0073] In one embodiment, the method further includes: Step S6: Receive the requirement adjustment information sent by the user, where the requirement adjustment information is used to represent that the user's requirement is adjusted from the first data of the target platform to the second data of the target platform; Step S7: The agent corrects the first encoding task based on the requirement adjustment information to obtain a third encoding task; Step S8: Generate a third MCP service component corresponding to the third encoding task through the agent. Step S9: Send a third acquisition request to the target platform through the third MCP service component, where the third acquisition request is used to acquire the second data. Step S10: When the second data sent by the target platform is received, register the third MCP service component to the intelligent computing center.

[0074] It should be noted that during the development of the MCP service component or after the MCP service component has been used for a period of time, the data that the user may need to obtain may change. At this time, optimization can be performed on the basis of the first MCP service component to obtain a third MCP service component that meets the user's changed requirements.

[0075] Specifically, receive the requirement adjustment information sent by the user, where the requirement adjustment information is used to represent the requirement to obtain the second data of the target platform; through the above-mentioned agent, correct the first encoding task based on the requirement adjustment information to obtain a third encoding task; through the agent, generate a third MCP service component corresponding to the third encoding task; send a third acquisition request to the target platform through the third MCP service component, where the third acquisition request is used to acquire the second data; when the second data sent by the target platform is received, register the third MCP service component to the intelligent computing center.

[0076] In this way, by correcting the first encoding task based on the requirement adjustment information to obtain a third encoding task, in order to generate a third MCP service component, when the third MCP service component can obtain the second data, register the third MCP service component to the intelligent computing center, so that the third MCP service component can meet the adjusted requirements of the user, and the flexibility of creating the MCP service component is improved.

[0077] The terms "first", "second", etc. in the present invention are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. In addition, in this application, "and / or" is used to represent at least one of the connected objects. For example, A and / or B and / or C represents 7 situations including A alone, B alone, C alone, A and B both present, B and C both present, A and C both present, and A, B, and C all present.

[0078] It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element.

[0079] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or a second terminal device, etc.) to execute the methods of the various embodiments of the present application.

[0080] The embodiments of the present application are described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them belong to the protection scope of the present application.

Claims

1. A method for realizing intelligent agent development MCP service through the computing power of intelligent computing center, characterized in that: include: Step S1, determining the interface information of the target application programming interface API, wherein the target API is the interface of the target platform; Step S2: Generate a first encoding task based on the interface information through the intelligent agent of the intelligent computing center; Step S3: Generate a first MCP service component corresponding to the first encoding task through the agent; Step S4: register the first MCP service component to the intelligent computing center.

2. The method according to claim 1, characterized in that The step S1 comprises: Step S11, receiving demand information sent by a user, wherein the demand information includes target demand data; Step S12: querying based on the target demand data to determine the target platform including the target demand data; Step S13: Obtain interface information of the target API of the target platform.

3. The method according to claim 1, characterized in that The step S1 comprises: Step S1', receiving the interface information of the target API sent by the target platform; After step S4, the method further includes: Step S5: Send the first MCP service component to the target platform.

4. The method according to any one of claims 1 to 3, characterized in that The step S2 comprises: Step S21, obtaining an MCP protocol template and a preset prompt word through the agent, wherein the MCP protocol template is obtained by the agent parsing the MCP; Step S22: The agent calls a large language model to generate the first encoding task based on the preset prompt words, the interface information and the MCP protocol template.

5. The method according to any one of claims 1 to 3, characterized in that The step S4 comprises: Step S41: Sending a first acquisition request to the target platform through the first MCP service component, where the first acquisition request is used to acquire first data; Step S42: upon receiving the first data sent by the target platform, registering the first MCP service component to the intelligent computing center.

6. The method according to claim 5, characterized in that The step S4 further comprises: Step S43: in the case where the first data sent by the target platform is not received, the first encoding task is modified by the agent based on the interface information to obtain a second encoding task; Step S44: Generate a second MCP service component corresponding to the second encoding task through the agent; Step S45: Sending a second acquisition request to the target platform through the second MCP service component, where the second acquisition request is used to acquire the first data; Step S46: upon receiving the first data sent by the target platform, registering the second MCP service component to the intelligent computing center.

7. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Step S6: receiving demand adjustment information sent by the user, wherein the demand adjustment information is used to indicate that the user's demand is adjusted from the first data of the target platform to the second data of the target platform; Step S7: using the agent, based on the demand adjustment information, modifying the first encoding task to obtain a third encoding task; Step S8: Generate a third MCP service component corresponding to the third encoding task through the agent; Step S9: sending a third acquisition request to the target platform through the third MCP service component, where the third acquisition request is used to acquire the second data; Step S10: upon receiving the second data sent by the target platform, registering the third MCP service component to the intelligent computing center.

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