redoc-mcp-server-azure

ryanturnberry/redoc-mcp-server-azure

3.2

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

Redoc MCP Azure

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:7071/mcp).

Project Structure

redoc-mcp-azure/
ā”œā”€ā”€ src/
ā”‚   ā”œā”€ā”€ index.ts              # Azure Functions entry point
ā”‚   ā””ā”€ā”€ server.ts             # MCP server implementation
ā”œā”€ā”€ host.json                 # Azure Functions host configuration
ā”œā”€ā”€ local.settings.json       # Azure Functions local settings
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