mcp-api-toolkit
Comprehensive API development and testing MCP server for Claude Code. Integrates OpenAPI parsing, testing, SDK generation, and documentation generation into Claude workflows.
README
๐ MCP API Toolkit
Comprehensive API development and testing MCP server for Claude Code
MCP API Toolkit brings powerful API development capabilities to Claude Code through the Model Context Protocol. Think Postman + OpenAPI + AI-powered testing, all integrated into your Claude workflow.
โจ Features
- ๐ OpenAPI/Swagger Parsing - Import and validate API specifications
- ๐งช API Testing - Execute and validate API requests with AI insights
- ๐ Documentation Generation - Auto-generate beautiful API docs
- ๐ ๏ธ SDK Generation - Create TypeScript, Python, JavaScript SDKs automatically
- ๐ญ Mock Data - Generate realistic test data from schemas
- โก Batch Testing - Test multiple endpoints at once
- ๐ Request Validation - Ensure requests/responses match schemas
- ๐ Performance Insights - Track response times and sizes
๐ฆ Installation
NPM (Recommended)
npm install -g mcp-api-toolkit
From Source
git clone https://github.com/yourusername/mcp-api-toolkit.git
cd mcp-api-toolkit
npm install
npm run build
npm link
๐ง Configuration
Add to your Claude Code MCP settings:
macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"api-toolkit": {
"command": "mcp-api-toolkit"
}
}
}
Or with npx:
{
"mcpServers": {
"api-toolkit": {
"command": "npx",
"args": ["-y", "mcp-api-toolkit"]
}
}
}
Restart Claude Code to activate the MCP server.
๐ฏ Usage
1. Parse OpenAPI Specification
Parse this OpenAPI spec: https://api.example.com/openapi.json
Or paste the spec directly:
Parse this OpenAPI spec:
{
"openapi": "3.0.0",
"info": { "title": "My API", "version": "1.0.0" },
...
}
2. List API Endpoints
List all endpoints from the parsed spec
3. Test an Endpoint
Test the GET /users endpoint with authentication header
Claude will use the test_api_endpoint tool:
GET https://api.example.com/users
Headers: { "Authorization": "Bearer token" }
4. Generate SDK
Generate a TypeScript SDK from the parsed spec
Claude will create a fully-typed client library:
export class MyAPIClient {
async getUsers(config?: RequestConfig): Promise<User[]> {
// Auto-generated implementation
}
}
5. Generate Documentation
Generate markdown documentation for this API
6. Create Mock Data
Generate 5 mock user objects based on this schema:
{
"type": "object",
"properties": {
"name": { "type": "string" },
"email": { "type": "string" },
"age": { "type": "number" }
}
}
๐ ๏ธ Available Tools
parse_openapi
Parse and validate OpenAPI/Swagger specifications (JSON, YAML, or URL).
Input:
input(string): OpenAPI spec as JSON/YAML or URLisUrl(boolean): Whether input is a URL
Output: Parsed specification with summary
list_endpoints
Extract all API endpoints from a specification.
Input:
spec(string): OpenAPI spec JSONfilterByTag(string, optional): Filter by tagfilterByMethod(string, optional): Filter by HTTP method
Output: List of endpoints with methods and paths
test_api_endpoint
Execute and validate API requests.
Input:
method(string): HTTP method (GET, POST, PUT, PATCH, DELETE)url(string): Full URL to testheaders(object, optional): HTTP headersbody(object, optional): Request bodyparams(object, optional): Query parametersexpectedStatus(number, optional): Expected HTTP statustimeout(number, optional): Timeout in ms (default: 30000)
Output: Response data, status, timing, validation results
generate_sdk
Generate client SDKs in multiple languages.
Input:
spec(string): OpenAPI spec JSONlanguage(string): typescript | python | javascript | goclientName(string, optional): Custom client class name
Output: Generated SDK code
generate_mock_data
Create mock data from JSON schemas.
Input:
schema(object): JSON Schema definitioncount(number, optional): Number of objects to generate
Output: Array of mock data objects
generate_api_docs
Generate markdown documentation from OpenAPI specs.
Input:
spec(string): OpenAPI spec JSONincludeExamples(boolean, optional): Include examples (default: true)
Output: Markdown documentation
validate_api_response
Validate responses against schemas.
Input:
response(object): API response dataschema(object): Expected schema
Output: Validation results
batch_test_endpoints
Test multiple endpoints at once.
Input:
spec(string): OpenAPI spec JSONbaseUrl(string, optional): Override base URLfilterByTag(string, optional): Test only tagged endpointsheaders(object, optional): Common headers
Output: Batch test results summary
๐ Examples
Example 1: Test a Public API
Parse the JSONPlaceholder API: https://jsonplaceholder.typicode.com/
Then test the GET /posts/1 endpoint
Example 2: Generate a Client Library
Parse this OpenAPI spec and generate a Python SDK:
{
"openapi": "3.0.0",
"info": { "title": "User API", "version": "1.0.0" },
"servers": [{ "url": "https://api.example.com" }],
"paths": {
"/users": {
"get": {
"summary": "List users",
"responses": {
"200": { "description": "Success" }
}
}
}
}
}
Example 3: API Testing Workflow
1. Parse the Stripe API spec: https://raw.githubusercontent.com/stripe/openapi/master/openapi/spec3.json
2. List all payment-related endpoints
3. Generate TypeScript SDK
4. Create mock customer data
๐จ Use Cases
API Development
- Import existing OpenAPI specs
- Test endpoints during development
- Generate client libraries automatically
- Create API documentation
API Testing
- Validate API responses
- Batch test endpoints for health checks
- Performance testing with timing metrics
- Mock data generation for testing
API Integration
- Generate SDKs for easy integration
- Test third-party APIs before integration
- Validate API contracts
- Document external APIs
Learning & Exploration
- Explore public APIs (GitHub, Stripe, Twitter, etc.)
- Understand API structures
- Generate working code examples
- Create educational documentation
๐๏ธ Architecture
mcp-api-toolkit/
โโโ src/
โ โโโ index.ts # Main MCP server
โ โโโ tools/ # MCP tool definitions
โ โโโ utils/
โ โ โโโ openapi-parser.ts # OpenAPI parsing
โ โ โโโ api-client.ts # HTTP client
โ โ โโโ sdk-generator.ts # SDK generation
โ โโโ types/
โ โโโ api.ts # TypeScript types
โโโ examples/ # Usage examples
โโโ docs/ # Documentation
โโโ tests/ # Unit tests
๐ Security
- No API credentials are stored
- All requests are made on-demand
- Rate limiting respect
- Input validation with Zod
- HTTPS-only for URL parsing
๐ค Contributing
Contributions are welcome! Please read our Contributing Guide first.
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Commit changes:
git commit -m 'Add amazing feature' - Push to branch:
git push origin feature/amazing-feature - Open a Pull Request
๐ License
MIT License - see LICENSE file for details
๐ Star History
If you find this project useful, please consider giving it a star on GitHub!
๐ Links
๐ก Inspiration
Built to solve the API-first development workflow in 2025. Inspired by:
- Postman's intuitive API testing
- OpenAPI's standardization
- Claude's AI-powered development assistance
๐ง Support
- Documentation: docs/
- Issues: GitHub Issues
- Discussions: GitHub Discussions
Made with โค๏ธ for the Claude Code community
Supercharge your API development workflow with AI!
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.