OpenAPI Schema MCP Server

What does “OpenAPI Schema” MCP Server do?

The OpenAPI Schema MCP Server is a Model Context Protocol (MCP) server designed to expose OpenAPI schema information to Large Language Models (LLMs) such as Claude. By providing structured access to OpenAPI specifications, this server enables AI assistants to explore and understand APIs, including their endpoints, parameters, request and response schemas, and more. This empowers developers and AI tools to query API structures, search across specifications, and retrieve detailed schema definitions, which enhances workflows involving API integration, documentation, and code generation. The server supports loading OpenAPI files in JSON or YAML format and provides results in YAML for improved LLM comprehension.

List of Prompts

No explicit prompt templates are documented in the repository.

List of Resources

No explicit resources are described in the repository.

List of Tools

The OpenAPI Schema MCP Server offers the following tools for LLMs:

• list-endpoints: Lists all API paths and their HTTP methods with summaries in a nested object structure.
• get-endpoint: Retrieves detailed information about a specific endpoint, including parameters and responses.
• get-request-body: Provides the request body schema for a specific endpoint and HTTP method.
• get-response-schema: Returns the response schema for a specific endpoint, method, and status code.
• get-path-parameters: Fetches the parameters for a specific path.
• list-components: Lists all schema components (schemas, responses, parameters, etc.).
• get-component: Returns the detailed definition for a specific component.
• list-security-schemes: Lists all available security schemes in the API.
• get-examples: Retrieves examples for a specific component or endpoint.
• search-schema: Searches across paths, operations, and schemas in the OpenAPI specification.

Use Cases of this MCP Server

• API Exploration and Documentation: Developers or LLMs can quickly list endpoints, parameters, and schemas, making it easier to document or understand an API’s structure.
• Automated Code Generation: By programmatically retrieving request and response schemas, tools can generate client libraries or code snippets for API interactions.
• API Testing and Validation: Test tools can use this server to fetch parameters and example payloads, supporting automated or manual API testing.
• Security Review: Analyze available security schemes and their implementation across endpoints to ensure APIs are properly secured.
• Schema Search and Analysis: Quickly find and analyze schema components or endpoints related to a specific entity or feature within large API specifications.

How to set it up

Windsurf

No setup instructions provided for Windsurf.

Claude

1. Ensure Node.js and npx are installed.
2. Locate your Claude Desktop configuration file:
   • macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: $env:AppData\Claude\claude_desktop_config.json
3. Add the OpenAPI Schema MCP server under the mcpServers object:

   {
     "mcpServers": {
       "OpenAPI Schema": {
         "command": "npx",
         "args": ["-y", "mcp-openapi-schema", "/ABSOLUTE/PATH/TO/openapi.yaml"]
       }
     }
   }
   

4. Save the file and restart Claude Desktop.
5. Verify the server is available in your Claude UI.

Cursor

No setup instructions provided for Cursor.

Cline

Claude Code CLI

1. Ensure Node.js and npx are installed.
2. Add the OpenAPI Schema MCP server:

   claude mcp add openapi-schema npx -y mcp-openapi-schema
   

   Or with a specific schema file:

   claude mcp add petstore-api npx -y mcp-openapi-schema ~/Projects/petstore.yaml
   

3. Verify registration:

   claude mcp list
   claude mcp get openapi-schema
   

4. Optionally, remove the server:

   claude mcp remove openapi-schema
   

5. Use the tools in your Claude Code session.

Securing API Keys

No information provided about securing API keys or using environment variables.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "MCP-name": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Based on the available documentation, the OpenAPI Schema MCP Server is highly specialized for OpenAPI exploration via LLMs, offering a strong toolset but lacking details on prompts, resources, API key handling, and advanced MCP features. For OpenAPI use-cases, it’s robust; for broader MCP features, it is limited.

MCP Score

Rating:
I would rate this MCP server a 6/10. While it is well-defined for OpenAPI schema exploration and offers a strong suite of tools, it lacks documentation for MCP prompt templates, explicit resource definitions, security best practices, and does not mention support for roots or sampling. The absence of a LICENSE is also a significant limitation for open collaboration.