SWAPI MCP Server

A Model Context Protocol (MCP) server that wraps the Star Wars API (SWAPI) as MCP tools, allowing LLMs and clients to search for Star Wars characters, planets, and films.

Features

• MCP-compliant server using the official TypeScript SDK

• Exposes three tools:

  • search_character: Search for a Star Wars character by name

  • get_planet: Get detailed planet info by ID

  • get_film: Get detailed film info by ID

• Supports both HTTP (stateless, streamable) and stdio transports

Code Structure

• index.ts: Main entry point. Sets up the MCP server, registers tools, and configures HTTP and stdio transports.

• test-client/: Example OpenAI client that demonstrates how to call the MCP server as a tool from an LLM.

Tool Details

search_character

• Input: { name: string }

• Description: Searches SWAPI for characters matching the given name.

• Returns: JSON-formatted list of matching characters.

get_planet

• Input: { id: string }

• Description: Fetches detailed info for a planet by its SWAPI ID.

• Returns: JSON-formatted planet details.

get_film

• Input: { id: string }

• Description: Fetches detailed info for a film by its SWAPI ID.

• Returns: JSON-formatted film details.

How It Works

• Uses the @modelcontextprotocol/sdk to create an MCP server.

• Each tool is registered with an input schema (using zod) and an async handler that fetches data from SWAPI.

• The server can be accessed via HTTP POST requests to /mcp or via stdio (for CLI clients).

Running the Server

Prerequisites

• Node.js v18 or newer

• npm

Install dependencies

npm install

Run in HTTP mode (default)

npx ts-node index.ts

• The server will listen on http://localhost:3000/mcp (or the port set in the PORT environment variable).

Run in stdio mode (for CLI clients)

• The server automatically starts a stdio transport for CLI-based MCP clients.

Example HTTP Request

List tools:

curl -X POST http://localhost:3000/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":0,"method":"tools/list","params":{}}'

Search for a character:

curl -N -X POST http://localhost:3000/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "search_character",
      "arguments": { "name": "Luke" }
    },
    "id": 1
  }'

Get a planet by ID:

curl -N -X POST http://localhost:3000/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "get_planet",
      "arguments": { "id": "1" }
    },
    "id": 2
  }'

Get a film by ID:

curl -N -X POST http://localhost:3000/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "get_film",
      "arguments": { "id": "1" }
    },
    "id": 3
  }'

Test Client: OpenAI + MCP Integration

The test-client/ directory contains an example script (swapi-client.ts) that demonstrates how to call the MCP server as a tool from an OpenAI LLM (e.g., GPT-4o-mini).

How it works

• Uses the OpenAI SDK and MCP tool integration.

• Registers the MCP server as a tool for the LLM.

• Sends a prompt/question to the LLM, which can call the MCP tools to answer.

Example: test-client/swapi-client.ts

// node --loader ts-node/esm ./swapi-client.ts
import 'dotenv/config';
import OpenAI from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY! });

const tools = [
  {
    type: "mcp" as const,
    server_label: 'swapi',
    server_url: 'http://localhost:3000/mcp', // this has to be accessible from your LLM
    require_approval: 'never' as const,
  },
];

const resp = await openai.responses.create({
  model: 'gpt-4o-mini',
  tools,
  input: 'Where was Luke Skywalker born and how tall is he?',
});

console.log(resp.output_text);

Running the test client

1. Install dependencies:

   cd test-client
   npm install

2. Set your OpenAI API key in a .env file:

   OPENAI_API_KEY=sk-...

3. Start the MCP server (in the parent directory):

   npx ts-node index.ts

4. Run the test client:

   node --loader ts-node/esm ./swapi-client.ts

• You should see the LLM's answer, which may include information fetched from the SWAPI MCP tools.

• You can also point the server_url to a public ngrok URL if you want to test from outside localhost.

Notes

• The server is stateless for HTTP requests (no session management).

• CORS is enabled for the mcp-session-id header.

• For more details on MCP, see the TypeScript SDK documentation.