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Swagger MCP

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Swagger MCP

Un servidor MCP que se conecta a una especificación Swagger y ayuda a una IA a construir todos los modelos necesarios para generar un servidor MCP para ese servicio.

Características

  • Descarga una especificación Swagger y la almacena localmente para una referencia más rápida.

  • Devuelve una lista de todos los puntos finales y sus métodos HTTP y descripciones.

  • Devuelve una lista de todos los modelos

  • Devuelve un modelo

  • Devuelve el servicio para conectarse al punto final.

  • Devuelve definiciones de funciones MCP

  • Genera definiciones completas de herramientas MCP con información completa del esquema

  • Incluye instrucciones específicas de IA en las descripciones de las herramientas.

Related MCP server: Mcp-Swagger-Server

Prerrequisitos

  • Node.js (v14 o superior)

  • npm o hilo

Instalación

  1. Clonar el repositorio:

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

  1. Instalar dependencias:

npm install

  1. Cree un archivo .env basado en el archivo .env.example :

cp .env.example .env

  1. Actualice el archivo .env .

Configuración

Edite el archivo .env para configurar la aplicación:

  • PORT : El puerto en el que se ejecutará el servidor (predeterminado: 3000)

  • NODE_ENV : El entorno (desarrollo, producción, prueba)

  • LOG_LEVEL : Nivel de registro (información, error, depuración)

Uso

Construyendo la aplicación

Construir la aplicación:

npm run build

Esto compilará el código TypeScript listo para usarse como un servidor MCP

Ejecutándose como un servidor MCP

Para ejecutarlo como servidor MCP para la integración con Cursor y otras aplicaciones:

node build/index.js

Uso del inspector MCP

Para ejecutar el inspector MCP para depuración:

npm run inspector

Agregar al cursor

Para agregar este servidor MCP a Cursor:

  1. Abra Configuración del cursor > Funciones > MCP

  2. Haga clic en "+ Agregar nuevo servidor MCP"

  3. Ingrese un nombre para el servidor (por ejemplo, "Swagger MCP")

  4. Seleccione "stdio" como tipo de transporte

  5. Ingrese el comando para ejecutar el servidor: node path/to/swagger-mcp/build/index.js y luego, si es necesario, agregue los argumentos de la línea de comando como se mencionó anteriormente.

  6. Haga clic en "Agregar"

Las herramientas Swagger MCP ahora estarán disponibles para el Agente de Cursor en Composer.

Herramientas Swagger MCP disponibles

Las siguientes herramientas están disponibles a través del servidor MCP:

  • getSwaggerDefinition : descarga una definición de Swagger desde una URL

  • listEndpoints : enumera todos los puntos finales de la definición de Swagger

  • listEndpointModels : enumera todos los modelos utilizados por un punto final específico

  • generateModelCode : genera código TypeScript para un modelo

  • generateEndpointToolCode : genera código TypeScript para una definición de herramienta MCP

Avisos Swagger MCP disponibles

El servidor también proporciona indicaciones MCP que guían a los asistentes de IA a través de flujos de trabajo comunes:

  • add-endpoint : una guía paso a paso para agregar un nuevo punto final utilizando las herramientas Swagger MCP

Para utilizar un aviso, los clientes pueden realizar una solicitud prompts/get con el nombre del aviso y argumentos opcionales:

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

El mensaje devolverá una serie de mensajes que guiarán al asistente de IA a través del proceso exacto necesario para agregar un nuevo punto final.

Configuración de su nuevo proyecto

Primero, pídale al agente que obtenga el archivo Swagger, asegúrese de darle la URL del archivo Swagger, o al menos una forma de encontrarlo para usted, esto descargará el archivo y lo guardará localmente con un nombre de archivo en hash, este nombre de archivo se agregará automáticamente a un archivo de configuración .swagger-mcp en la raíz de su solución actual.

Archivo de configuración .swagger-mcp generado automáticamente

SWAGGER_FILENAME = TheFilenameOfTheLocallyStoredSwaggerFile

Este simple archivo de configuración asocia su proyecto actual con una API Swagger específica; podremos usarlo para almacenar más detalles en el futuro.

Una vez configurado, el MCP podrá encontrar su definición de Swagger y asociarla con su solución actual, reduciendo la cantidad de llamadas API necesarias para obtener el proyecto y las tareas relacionadas con la solución en la que está trabajando.

Generador de código de herramientas MCP mejorado

Se ha mejorado el generador de código de herramientas MCP para proporcionar definiciones de herramientas más completas y utilizables:

Mejoras clave

  1. Información de esquema completa : el generador ahora incluye información de esquema completa para todos los modelos, incluidos los objetos anidados, directamente en el inputSchema.

  2. Mejor denominación de parámetros : los nombres de parámetros ahora son más semánticos y evitan caracteres problemáticos como los puntos (por ejemplo, taskRequest en lugar de task.Request ).

  3. Nombres de herramientas semánticas : los nombres de herramientas ahora son más descriptivos y siguen convenciones de nomenclatura consistentes basadas en el método HTTP y la ruta del recurso.

  4. Compatibilidad con archivos Swagger YAML : el generador ahora admite archivos de definición Swagger JSON y YAML.

  5. Documentación mejorada : las definiciones de herramientas generadas incluyen descripciones completas de todos los parámetros y propiedades.

  6. Sin dependencias externas : el código generado no requiere la importación de archivos de modelos externos, lo que lo hace más autónomo y más fácil de usar.

  7. Instrucciones específicas de IA : las descripciones de las herramientas ahora incluyen instrucciones especiales para los agentes de IA, lo que les ayuda a comprender cómo usar las herramientas de manera eficaz.

Ejemplo de uso

Para generar una definición de herramienta MCP para un punto final:

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

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

console.log(toolCode);

Esto generará una definición completa de la herramienta MCP con información de esquema completa para el punto final POST /pets.

Licencia

Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.

Indicaciones de MCP para asistentes de IA

Para ayudar a los asistentes de IA a usar las herramientas Swagger MCP eficazmente, hemos creado una colección de indicaciones que los guían en tareas comunes. Estas indicaciones proporcionan instrucciones paso a paso para procesos como añadir nuevos puntos finales, usar modelos generados y más.

Consulte el archivo PROMPTS.md para ver la colección completa de indicaciones.

Ejemplo de caso de uso: cuando le pides a un asistente de IA que agregue un nuevo punto final a tu proyecto, puedes hacer referencia al mensaje "Agregar un nuevo punto final" para asegurarte de que el asistente siga el proceso correcto en el orden correcto.

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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