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openapi-mcp-bridge

openapi-mcp-bridge

by ephrin

OpenAPI MCP Bridge

npm version TypeScript License: MIT

Transform OpenAPI definitions into MCP (Model Context Protocol) tools for seamless LLM-API integration.

What is MCP?

Model Context Protocol (MCP) is a standard protocol that allows AI models to interact with external tools and data sources. Unlike REST APIs that use HTTP requests, MCP uses JSON-RPC messages over stdio or WebSocket connections.

Key Differences:

  • REST API: HTTP requests → JSON responses

  • MCP: JSON-RPC messages → Tool calls and responses

  • Purpose: MCP bridges AI models with external systems safely and efficiently

Why OpenAPI → MCP?

  • Your APIs are already documented in OpenAPI format

  • AI models can't directly call REST APIs

  • MCP provides a secure, standardized way to expose API functionality to AI

Related MCP server: openapi-mcp-server

Quick Start (30 seconds)

1. Install and Run

npm install -g openapi-mcp-bridge
mkdir my-api && cd my-api

2. Create OpenAPI Definition

cat > museum-api.yaml << 'EOF'
openapi: 3.1.0
info:
  title: Museum API
  version: 1.0.0
servers:
  - url: https://redocly.com/_mock/demo/openapi/museum-api
paths:
  /museum-hours:
    get:
      summary: Get museum hours
      operationId: getMuseumHours
      parameters:
        - name: date
          in: query
          schema:
            type: string
            format: date
components:
  securitySchemes:
    BasicAuth:
      type: http
      scheme: basic
EOF

3. Test with MCP Inspector

# Terminal 1: Start MCP server
openapi-mcp-bridge --definitions .

# Terminal 2: Test with inspector
npm install -g @modelcontextprotocol/inspector
mcp-inspector npx openapi-mcp-bridge --definitions .

Result: You'll see getMuseumHours tool available in the MCP Inspector interface.

Integration Examples

Claude Desktop Integration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "museum-api": {
      "command": "npx",
      "args": ["openapi-mcp-bridge", "--definitions", "/path/to/your/api-definitions"]
    }
  }
}

Usage: Ask Claude "What are the museum hours?" and it will automatically call your API.

Claude Code Integration

  1. Create .claude-code-mcp.json in your project:

{
  "mcpServers": {
    "my-api": {
      "command": "npx",
      "args": ["openapi-mcp-bridge", "--definitions", "./api-definitions"]
    }
  }
}
  1. Claude Code will automatically detect and use your API tools.

Custom MCP Client

// TypeScript example with proper ES module setup
import { spawn } from 'child_process';
import { MCPClient } from '@modelcontextprotocol/client';

const serverProcess = spawn('npx', ['openapi-mcp-bridge', '--definitions', './api-definitions']);
const client = new MCPClient();

await client.connect({ 
  stdio: { 
    stdin: serverProcess.stdin, 
    stdout: serverProcess.stdout 
  } 
});

// List available tools
const tools = await client.listTools();
console.log('Available tools:', tools);

// Call a tool
const result = await client.callTool('getMuseumHours', { date: '2024-01-15' });
console.log('Result:', result);

Usage Patterns

When to Use Each Approach

Use Case

Approach

Best For

AI Model Integration

CLI (openapi-mcp-bridge)

Claude Desktop, Claude Code, custom MCP clients

Web Application

Express/Fastify middleware

Adding MCP endpoints to existing web apps

Microservice

Standalone server

Dedicated MCP service, Docker deployments

Development/Testing

MCP Inspector

Testing and debugging MCP tools

Decision Tree

Do you want to integrate with an AI model?
├── Yes → Use CLI approach
│   ├── Claude Desktop → Update claude_desktop_config.json
│   ├── Claude Code → Use `claude mcp add` command
│   └── Custom client → Use stdio connection
└── No → Use HTTP approach
    ├── Existing Express app → Use Express middleware
    ├── New microservice → Use standalone server
    └── Testing → Use MCP Inspector

Transport Mode Decision Matrix

Transport

Use Case

Pros

Cons

Best For

stdio

AI model integration

Simple, secure, no network config

Single process, local only

Claude Desktop, Claude Code, development

HTTP

Web applications

Multi-user, remote access, familiar

Network setup, security concerns

Production APIs, microservices

WebSocket

Real-time updates

Bi-directional, low latency

Complex setup, connection management

Streaming, live data

stdio is the recommended transport for AI model integration because:

  • ✅ Security: No network exposure or authentication needed

  • ✅ Simplicity: Direct process communication

  • ✅ Performance: Lower overhead than HTTP

  • ✅ Reliability: No network connectivity issues

  • ✅ Lifecycle: Automatic process management

Troubleshooting

Common Issues

1. Import Path Errors

# ❌ Error: Cannot find module 'openapi-mcp-bridge/express'
import { createExpressMiddleware } from 'openapi-mcp-bridge/express';

# ✅ Solution: Use the correct package exports
import { createExpressMiddleware } from 'openapi-mcp-bridge/express';

Root Cause: Package uses ES modules. Ensure your package.json has "type": "module".

2. "Cannot POST /mcp" Error

# ❌ Wrong: Trying to make HTTP requests to MCP endpoint
curl -X POST http://localhost:3000/mcp

# ✅ Right: Use MCP Inspector or MCP client
mcp-inspector http://localhost:3000/mcp

Root Cause: MCP is not a REST API. It uses JSON-RPC over stdio/WebSocket.

3. Port Conflicts

# ❌ Error: EADDRINUSE: address already in use :::3000
npm start

# ✅ Solution: Use a different port
PORT=3001 npm start
# or
npx openapi-mcp-bridge --definitions . --port 3001

4. CLI Warnings

# ❌ Warning: --port is not yet implemented in stdio mode
openapi-mcp-bridge --definitions . --port 3000

# ✅ Solution: Don't use --port with CLI (stdio mode)
openapi-mcp-bridge --definitions .

Root Cause: CLI runs in stdio mode for MCP clients. Use standalone server for HTTP mode.

5. Module Import Issues

// ❌ CommonJS in ES module project
const { createExpressMiddleware } = require('openapi-mcp-bridge/express');

// ✅ ES modules syntax
import { createExpressMiddleware } from 'openapi-mcp-bridge/express';

Setup for TypeScript projects:

// package.json
{
  "type": "module",
  "scripts": {
    "start": "tsx src/server.ts"
  }
}
// tsconfig.json
{
  "compilerOptions": {
    "module": "ES2022",
    "moduleResolution": "node",
    "target": "ES2022",
    "esModuleInterop": true,
    "allowSyntheticDefaultImports": true
  }
}

Debug Mode

Enable detailed logging:

# CLI
openapi-mcp-bridge --definitions . --debug

# Environment variable
DEBUG=true openapi-mcp-bridge --definitions .

# Programmatic
const config = {
  logging: { consoleFallback: true },
  debug: true
};

Validation Issues

# Check if OpenAPI file is valid
npx @redocly/cli lint your-api.yaml

# Force cache regeneration
OPENAPI_FORCE_REGEN=true openapi-mcp-bridge --definitions .

# Test tool generation
mcp-inspector npx openapi-mcp-bridge --definitions .

Advanced Usage

Express Integration

// server.ts
import express from 'express';
import { createExpressMiddleware } from 'openapi-mcp-bridge/express';

const app = express();

// Add MCP endpoint
app.use('/mcp', createExpressMiddleware({
  definitionsDirectory: './api-definitions',
  defaultCredentials: {
    username: process.env.API_USERNAME,
    password: process.env.API_PASSWORD
  }
}));

// Add health check
app.get('/health', (req, res) => {
  res.json({ status: 'healthy' });
});

app.listen(3000, () => {
  console.log('MCP server: http://localhost:3000/mcp');
});

Standalone Server

import { MCPServer } from 'openapi-mcp-bridge';

const server = new MCPServer({
  definitionsDirectory: './api-definitions',
  port: 3000,
  mountPath: '/mcp',
  defaultCredentials: {
    username: process.env.API_USERNAME,
    password: process.env.API_PASSWORD
  }
});

await server.start();
console.log('MCP server running on http://localhost:3000/mcp');

Configuration

interface Config {
  definitionsDirectory: string;
  cacheDirectory?: string;
  defaultCredentials?: {
    username?: string;
    password?: string;
    token?: string;
    apiKey?: string;
  };
  logging?: {
    winston?: any;
    pino?: any;
    consoleFallback?: boolean;
  };
  mcpOptions?: {
    serverName?: string;
    serverVersion?: string;
  };
}

🏷️ Tool Naming & Discoverability

Naming Patterns

The library generates predictable tool names from OpenAPI operations:

OpenAPI Operation

Generated Tool Name

Rule

GET /museum-hours with operationId: getMuseumHours

getMuseumHours

Uses operationId when available

POST /special-events with operationId: createSpecialEvent

createSpecialEvent

Uses operationId when available

GET /events/{eventId}

get-events-by-eventId

Auto-generated: {method}-{path}-by-{param}

DELETE /tickets/{ticketId}

delete-tickets-by-ticketId

Auto-generated: {method}-{path}-by-{param}

PATCH /users/{userId}/profile

patch-users-by-userId-profile

Auto-generated: handles nested paths

Custom Tool Names

Override generated names using customization:

# museum-api.custom.yaml
toolAliases:
  "getMuseumHours": "get-hours"
  "createSpecialEvent": "create-event"
  "get-events-by-eventId": "get-event-details"

Tool Discovery

List available tools programmatically:

# Using MCP Inspector
mcp-inspector npx openapi-mcp-bridge --definitions ./api-definitions

# In Claude Code
"What tools are available?"

# In Claude Desktop
"List all museum API tools"

📁 Project Structure

your-project/
├── api-definitions/
│   ├── museum-api.yaml          # OpenAPI specification
│   ├── museum-api.custom.yaml   # Optional customization
│   └── .cache/                  # Auto-generated cache
├── src/
│   └── server.ts               # Your server code
├── package.json                # {"type": "module"}
└── tsconfig.json               # ES2022 modules

🔐 Authentication

Supports HTTP Basic, Bearer tokens, and API keys:

# museum-api.custom.yaml
authenticationOverrides:
  - endpoint: "*"
    credentials:
      username: "${API_USERNAME}"
      password: "${API_PASSWORD}"

🧪 Testing

# Test tool generation
npm install -g @modelcontextprotocol/inspector
mcp-inspector npx openapi-mcp-bridge --definitions ./api-definitions

# Validate OpenAPI specs
npx @redocly/cli lint api-definitions/*.yaml

# Test with real API calls
node -e "
import { MCPClient } from '@modelcontextprotocol/client';
// ... client code
"

📝 Examples

🤝 Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Add tests for new functionality

  4. Ensure all tests pass

  5. Submit a pull request

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments


Need help? Check our troubleshooting guide or open an issue.

Available Tools

8 tools
buy-ticketsC

Buy museum tickets

Inputs: body (required).

create-eventC

Create special events

Inputs: body (required).

delete-eventC

Delete special event

Inputs: eventId (required).

get-event-detailsC

Get special event

Inputs: eventId (required).

get-ticket-qrB

Get ticket QR code

Inputs: ticketId (required).

list-eventsC

List special events

Inputs: page, limit, endDate, startDate.

list-museum-hoursC

Get museum hours

Inputs: page, limit, startDate.

update-eventC

Update special event

Inputs: body (required), eventId (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.1/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool targets a distinct function: CRUD for events, listing hours, and ticket operations. No overlap between tools.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with underscores, e.g., update-event, list-museum-hours, buy-tickets.

    Tool Count5/5

    8 tools is well-scoped for a museum domain covering events, hours, and ticket purchase/QR retrieval.

    Completeness4/5

    Events have full CRUD, museum hours listed, tickets covered for purchase and QR. Missing cancellation or history of tickets, but core is complete.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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