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Swagger MCP

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Swagger MCP

Сервер MCP, который подключается к спецификации Swagger и помогает ИИ создавать все необходимые модели для создания сервера MCP для этой службы.

Функции

  • Загружает спецификацию Swagger и сохраняет ее локально для более быстрого использования.

  • Возвращает список всех конечных точек, их HTTP-методов и описаний.

  • Возвращает список всех моделей

  • Возвращает модель

  • Возвращает услугу для подключения к конечной точке

  • Возвращает определения функций MCP

  • Создает полные определения инструментов MCP с полной информацией о схеме

  • Включает в описания инструментов инструкции, специфичные для ИИ

Related MCP server: Mcp-Swagger-Server

Предпосылки

  • Node.js (v14 или выше)

  • нпм или пряжа

Установка

  1. Клонируйте репозиторий:

git clone https://github.com/readingdancer/swagger-mcp.git
cd swagger-mcp

  1. Установите зависимости:

npm install

  1. Создайте файл .env на основе файла .env.example :

cp .env.example .env

  1. Обновите файл .env .

Конфигурация

Отредактируйте файл .env для настройки приложения:

  • PORT : Порт, на котором будет работать сервер (по умолчанию: 3000)

  • NODE_ENV : Среда (разработка, производство, тестирование)

  • LOG_LEVEL : Уровень ведения журнала (информация, ошибка, отладка)

Использование

Создание приложения

Создайте приложение:

npm run build

Это скомпилирует код TypeScript, готовый к использованию в качестве сервера MCP.

Работает как сервер MCP

Для запуска в качестве сервера MCP для интеграции с Cursor и другими приложениями:

node build/index.js

Использование MCP-инспектора

Чтобы запустить MCP Inspector для отладки:

npm run inspector

Добавление к курсору

Чтобы добавить этот MCP-сервер в Cursor:

  1. Откройте Настройки курсора > Функции > MCP

  2. Нажмите «+ Добавить новый сервер MCP»

  3. Введите имя сервера (например, «Swagger MCP»)

  4. Выберите «stdio» в качестве типа транспорта.

  5. Введите команду для запуска сервера: node path/to/swagger-mcp/build/index.js а затем, если необходимо, добавьте аргументы командной строки, как указано выше.

  6. Нажмите «Добавить».

Инструменты Swagger MCP теперь будут доступны для Cursor Agent в Composer.

Доступные инструменты Swagger MCP

На сервере MCP доступны следующие инструменты:

  • getSwaggerDefinition : загружает определение Swagger с URL-адреса

  • listEndpoints : список всех конечных точек из определения Swagger

  • listEndpointModels : список всех моделей, используемых определенной конечной точкой.

  • generateModelCode : Генерирует код TypeScript для модели

  • generateEndpointToolCode : генерирует код TypeScript для определения инструмента MCP

Доступные подсказки Swagger MCP

Сервер также предоставляет подсказки MCP, которые направляют помощников ИИ по общим рабочим процессам:

  • add-endpoint : пошаговое руководство по добавлению новой конечной точки с использованием инструментов Swagger MCP

Чтобы использовать подсказку, клиенты могут сделать запрос prompts/get с именем подсказки и необязательными аргументами:

{
  "method": "prompts/get",
  "params": {
    "name": "add-endpoint",
    "arguments": {
      "swaggerUrl": "https://petstore.swagger.io/v2/swagger.json",
      "endpointPath": "/pets/{id}",
      "httpMethod": "GET"
    }
  }
}

В ответ на запрос будет выведен ряд сообщений, которые проведут помощника на основе искусственного интеллекта через точный процесс, необходимый для добавления новой конечной точки.

Настройка вашего нового проекта

Сначала попросите агента получить файл Swagger, обязательно сообщите ему URL-адрес файла Swagger или, по крайней мере, способ его найти. Это позволит загрузить файл и сохранить его локально с хешированным именем файла. Это имя файла будет автоматически добавлено в файл настроек .swagger-mcp в корне вашего текущего решения.

Автоматически сгенерированный файл конфигурации .swagger-mcp

SWAGGER_FILENAME = TheFilenameOfTheLocallyStoredSwaggerFile

Этот простой файл конфигурации связывает ваш текущий проект с определенным API Swagger. В будущем мы можем использовать его для хранения дополнительных сведений.

После настройки MCP сможет найти ваше определение Swagger и связать его с вашим текущим решением, что сократит количество вызовов API, необходимых для получения проекта и задач, связанных с решением, над которым вы работаете.

Улучшенный генератор кода инструмента MCP

Генератор кода инструмента MCP был улучшен для предоставления более полных и удобных определений инструментов:

Ключевые улучшения

  1. Полная информация о схеме : генератор теперь включает полную информацию о схеме для всех моделей, включая вложенные объекты, непосредственно в inputSchema.

  2. Улучшенное именование параметров : имена параметров теперь более семантические и не содержат проблемных символов, таких как точки (например, taskRequest вместо task.Request ).

  3. Семантические имена инструментов : имена инструментов теперь более описательные и соответствуют единым соглашениям об именовании, основанным на методе HTTP и пути к ресурсу.

  4. Поддержка файлов YAML Swagger : генератор теперь поддерживает файлы определений Swagger как JSON, так и YAML.

  5. Улучшенная документация : созданные определения инструментов включают в себя исчерпывающие описания всех параметров и свойств.

  6. Отсутствие внешних зависимостей : сгенерированный код не требует импорта внешних файлов модели, что делает его более автономным и простым в использовании.

  7. Инструкции, специальные для ИИ : описания инструментов теперь включают специальные инструкции для агентов ИИ, помогающие им понять, как эффективно использовать инструменты.

Пример использования

Чтобы создать определение инструмента MCP для конечной точки:

import generateEndpointToolCode from './services/generateEndpointToolCode.js';

const toolCode = await generateEndpointToolCode({
  path: '/pets',
  method: 'POST',
  swaggerFilePath: './petstore.json',
  singularizeResourceNames: true
});

console.log(toolCode);

Это позволит создать полное определение инструмента MCP с полной информацией о схеме для конечной точки POST /pets.

Лицензия

Данный проект лицензирован по лицензии MIT — подробности см. в файле LICENSE .

MCP предлагает использовать помощников с искусственным интеллектом

Чтобы помочь помощникам ИИ эффективно использовать инструменты Swagger MCP, мы создали набор подсказок, которые направляют их через общие задачи. Эти подсказки предоставляют пошаговые инструкции для таких процессов, как добавление новых конечных точек, использование сгенерированных моделей и многое другое.

Полную коллекцию подсказок смотрите в файле PROMPTS.md .

Пример использования: когда вы просите помощника на основе искусственного интеллекта добавить новую конечную точку в ваш проект, вы можете сослаться на подсказку «Добавление новой конечной точки», чтобы убедиться, что помощник выполнит правильный процесс в правильном порядке.

Available Tools

5 tools
execute_api_requestC

Execute an API request to a specific endpoint

ParametersJSON Schema
NameRequiredDescriptionDefault
methodYesHTTP method (GET, POST, PUT, DELETE, etc.)
pathYesThe endpoint path (e.g., '/users/123')
paramsNoQuery parameters as key-value pairs
bodyNoRequest body as a JSON object (for POST/PUT/PATCH)
headersNoCustom headers as key-value pairs

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool executes an API request but fails to describe critical traits such as authentication requirements, error handling, rate limits, side effects (e.g., whether it modifies data), or response format. This leaves significant gaps for a tool that could perform destructive operations like DELETE.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that gets straight to the point without unnecessary words. It's appropriately sized for a general-purpose tool, though it could be more front-loaded with key details if it were more comprehensive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of executing arbitrary API requests (with potential for mutations, auth needs, etc.), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects, return values, or usage context, making it inadequate for safe and effective tool selection by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents all 5 parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify parameter interactions or provide examples), resulting in a baseline score of 3 where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('execute') and resource ('API request to a specific endpoint'), which provides a basic purpose. However, it lacks specificity about what kind of API or system this targets, and it doesn't clearly differentiate from sibling tools like 'fetch_swagger_info' or 'validate_api_response', which might involve similar API interactions but for different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., for testing or production calls), or exclusions, leaving the agent to infer usage based on the generic name and parameters alone, which is insufficient given the presence of sibling tools like 'get_endpoint_details' or 'list_endpoints'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fetch_swagger_infoC

Fetch Swagger/OpenAPI documentation to discover available API endpoints

ParametersJSON Schema
NameRequiredDescriptionDefault
urlNoURL to the swagger.json or swagger.yaml file. If not provided, will try to use the base URL with common Swagger paths.

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves—such as whether it makes network requests, handles errors, returns structured data, or has any side effects. This leaves significant gaps for an agent to understand the tool's operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex API documentation. It doesn't explain what the output looks like (e.g., JSON/YAML structure), potential errors, or how it interacts with sibling tools, leaving the agent with insufficient context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the schema already documents the single parameter ('url') adequately. The description adds no additional meaning or context about the parameter beyond what's in the schema, such as examples or constraints, but this is acceptable given the high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('fetch') and resource ('Swagger/OpenAPI documentation'), and indicates what it's used for ('to discover available API endpoints'). However, it doesn't explicitly differentiate this from sibling tools like 'list_endpoints' or 'get_endpoint_details', which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'list_endpoints' or 'get_endpoint_details'. It mentions the purpose but doesn't specify scenarios, prerequisites, or exclusions that would help an agent choose between these related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_endpoint_detailsB

Get detailed information about a specific API endpoint

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesThe endpoint path to get details for (e.g., '/users/{id}')
methodYesThe HTTP method (GET, POST, PUT, DELETE, etc.)

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information, implying a read-only operation, but doesn't specify aspects like authentication requirements, rate limits, error handling, or the format of the returned details. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by directly stating the tool's function, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It clarifies the purpose but lacks behavioral details and usage guidelines. Without an output schema, it doesn't explain return values, which could be a gap, but the description focuses on the input aspect, making it borderline complete for a basic read operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with clear descriptions for both parameters ('path' and 'method'), so the schema does the heavy lifting. The description adds no additional parameter semantics beyond implying that these inputs identify a 'specific API endpoint', which is already inferred from the schema. Baseline 3 is appropriate as the schema provides adequate documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('detailed information about a specific API endpoint'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'fetch_swagger_info' or 'list_endpoints', which likely provide similar API information but with different scopes or formats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'fetch_swagger_info' (which might retrieve broader API documentation) and 'list_endpoints' (which might list endpoints without details), the description lacks context for selection, leaving the agent to infer usage based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_endpointsB

List all available API endpoints after fetching Swagger documentation

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that endpoints are listed 'after fetching Swagger documentation', hinting at a dependency or sequence, but it doesn't describe what 'list' entails (e.g., format, pagination, or if it's a read-only operation). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('List all available API endpoints') and adds necessary context ('after fetching Swagger documentation'). There is no wasted verbiage, and every part of the sentence contributes to understanding the tool's purpose and sequence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity is low (0 parameters, no output schema), the description is adequate but has gaps. It covers the purpose and hints at a sequence, but without annotations or output schema, it lacks details on behavior (e.g., what 'list' returns, any side effects). For a simple listing tool, it's minimally viable but could be more complete by clarifying the output or dependencies.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it appropriately doesn't mention any. Baseline is 4 for 0 parameters, as the description doesn't introduce confusion or redundancy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List all available API endpoints') and the resource ('API endpoints'), making the purpose immediately understandable. It distinguishes itself from siblings like 'fetch_swagger_info' by specifying it operates 'after fetching Swagger documentation', though it doesn't explicitly contrast with all siblings like 'get_endpoint_details'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by mentioning 'after fetching Swagger documentation', suggesting a prerequisite or sequence, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_endpoint_details' or 'execute_api_request'. No exclusions or clear alternatives are stated, leaving usage context somewhat vague.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_api_responseB

Validate an API response against the schema from Swagger documentation

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesThe endpoint path
methodYesThe HTTP method
statusCodeYesThe HTTP status code
responseBodyYesThe response body to validate

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates responses but doesn't explain how validation works (e.g., returns validation errors, success/failure status), what happens on failure, or any side effects. For a validation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, validation logic) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose but fails to provide critical context like validation outcomes, error handling, or integration with sibling tools, leaving gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, clearly documenting all four parameters (path, method, statusCode, responseBody). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Validate an API response against the schema from Swagger documentation'. It specifies the verb 'validate' and the resource 'API response', making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'execute_api_request' or 'fetch_swagger_info', which might handle related but distinct operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing Swagger documentation loaded), context (e.g., after an API call), or exclusions. With siblings like 'execute_api_request' and 'fetch_swagger_info', this lack of differentiation leaves usage ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 5 tool updates
    • First observedexecute_api_request
    • First observedfetch_swagger_info
    • First observedget_endpoint_details
    • First observedlist_endpoints
    • First observedvalidate_api_response
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A3.5/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: fetching documentation, listing endpoints, getting endpoint details, executing requests, and validating responses. The descriptions make it easy to tell them apart, as they target different stages of the API workflow.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., fetch_swagger_info, list_endpoints, execute_api_request). The naming is predictable and readable throughout, with no deviations in style or convention.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose of interacting with Swagger/OpenAPI documentation and APIs. Each tool earns its place by covering distinct aspects of the workflow, from discovery to execution and validation.

Completeness5/5

The tool set provides complete coverage for the domain: it supports fetching documentation, listing and detailing endpoints, executing requests, and validating responses. There are no obvious gaps, and agents can handle the full API lifecycle without dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues

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