openapi-yaml-to-html-mcp-server

ryanturnberry/openapi-yaml-to-html-mcp-server

3.2

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Redoc MCP is an MCP server built with ModelFetch, designed to facilitate interaction with LLMs through tools, resources, and prompts.

Tools
1
Resources
0
Prompts
0

Redoc MCP

An MCP server built with ModelFetch

Quick Start

Running the MCP server

Run your MCP server locally:

npm start

Testing with the MCP Inspector

Run the MCP Inspector locally:

npx -y @modelcontextprotocol/inspector@latest

Then, connect to your MCP server using Streamable HTTP transport (default URL: http://localhost:3000/mcp).

Project Structure

redoc_mcp/
ā”œā”€ā”€ src/
ā”‚   ā”œā”€ā”€ index.ts      # Node.js entry point
ā”‚   ā””ā”€ā”€ server.ts     # MCP server implementation
ā”œā”€ā”€ tsconfig.json
ā”œā”€ā”€ package.json
ā””ā”€ā”€ README.md

Adding Features

Tools

Tools provide executable functions to LLMs:

server.registerTool(
  "my_tool",
  {
    title: "My Tool",
    description: "What this tool does",
    inputSchema: { param: z.string() },
  },
  ({ param }) => ({
    content: [
      {
        type: "text",
        text: `Result for ${param}`,
      },
    ],
  }),
);

Resources

Resources expose data and content to LLMs:

server.registerResource(
  "my_resource",
  "resource://my-resource",
  {
    title: "My Resource",
    description: "What this resource does",
    mimeType: "text/plain",
  },
  (uri) => ({
    contents: [
      {
        uri: uri.href,
        text: "Content of my resource",
      },
    ],
  }),
);

Prompts

Prompts are reusable templates for interacting with LLMs:

server.registerPrompt(
  "my_prompt",
  {
    title: "My Prompt",
    description: "What this prompt does",
    argsSchema: { arg: z.string() },
  },
  ({ arg }) => ({
    messages: [
      {
        role: "user",
        content: {
          type: "text",
          text: `Prompt with ${arg}`,
        },
      },
    ],
  }),
);

Reading Docs