
APIWeaver
Converts any REST API into MCP tools that AI assistants can use by registering API configurations at runtime. Supports multiple authentication methods and automatically generates callable tools from API endpoints.
Dynamically converts any REST API into MCP tools by registering web API configurations at runtime. Supports multiple authentication methods and automatically generates MCP-compatible tools for AI assistants to interact with external web services.
What it does
- Register REST APIs dynamically at runtime
- Generate MCP tools from API endpoints automatically
- Authenticate with Bearer tokens, API keys, Basic auth, and OAuth2
- Call registered API endpoints with dynamic parameters
- Test API connections before use
- Get API schemas and endpoint documentation
Best for
About APIWeaver
APIWeaver is a community-built MCP server published by gongrzhe that provides AI assistants with tools and capabilities via the Model Context Protocol. APIWeaver converts any REST API into MCP tools at runtime, supporting multiple auth methods and auto-generating MCP-comp It is categorized under developer tools. This server exposes 6 tools that AI clients can invoke during conversations and coding sessions.
How to install
You can install APIWeaver in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
APIWeaver is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Tools (6)
Register a new API configuration and create MCP tools for its endpoints. Args: config: API configuration dictionary containing: - name: API name - base_url: Base URL for the API - description: Optional API description - auth: Optional authentication configuration - headers: Optional global headers - endpoints: List of endpoint configurations Returns: Success message with list of created tools
List all registered APIs and their endpoints. Returns: Dictionary of registered APIs with their configurations
Unregister an API and remove its tools. Args: api_name: Name of the API to unregister Returns: Success message
Test connection to a registered API. Args: api_name: Name of the API to test Returns: Connection test results
Call any registered API endpoint with dynamic parameters. This is a generic tool that allows calling any registered API endpoint without having to use the specific endpoint tools. Useful for ad-hoc API calls or when you want more flexibility. Args: api_name: Name of the registered API to call endpoint_name: Name of the endpoint within the API parameters: Dictionary of parameters to pass to the endpoint ctx: Optional context for logging Returns: API response data and metadata Example: # Call OpenWeatherMap API result = await call_api( api_name="OpenWeatherMap", endpoint_name="get_weather", parameters={"q": "London", "units": "metric"} ) # Call GitHub API result = await call_api( api_name="GitHub", endpoint_name="get_user", parameters={"username": "octocat"} )
APIWeaver
A FastMCP server that dynamically creates MCP (Model Context Protocol) servers from web API configurations. This allows you to easily integrate any REST API, GraphQL endpoint, or web service into an MCP-compatible tool that can be used by AI assistants like Claude.
Features
- π Dynamic API Registration: Register any web API at runtime
- π Multiple Authentication Methods: Bearer tokens, API keys, Basic auth, OAuth2, and custom headers
- π οΈ All HTTP Methods: Support for GET, POST, PUT, DELETE, PATCH, and more
- π Flexible Parameters: Query params, path params, headers, and request bodies
- π Automatic Tool Generation: Each API endpoint becomes an MCP tool
- π§ͺ Built-in Testing: Test API connections before using them
- π Response Handling: Automatic JSON parsing with fallback to text
- π Multiple Transport Types: STDIO, SSE, and Streamable HTTP transport support
Transport Types
APIWeaver supports three different transport types to accommodate various deployment scenarios:
STDIO Transport (Default)
- Usage:
apiweaver runorapiweaver run --transport stdio - Best for: Local tools, command-line usage, and MCP clients that connect via standard input/output
- Characteristics: Direct process communication, lowest latency, suitable for desktop applications
- Endpoint: N/A (uses stdin/stdout)
SSE Transport (Legacy)
- Usage:
apiweaver run --transport sse --host 127.0.0.1 --port 8000 - Best for: Legacy MCP clients that only support Server-Sent Events
- Characteristics: HTTP-based, one-way streaming from server to client
- Endpoint:
http://host:port/mcp - Note: This transport is deprecated in favor of Streamable HTTP
Streamable HTTP Transport (Recommended)
- Usage:
apiweaver run --transport streamable-http --host 127.0.0.1 --port 8000 - Best for: Modern web deployments, cloud environments, and new MCP clients
- Characteristics: Full HTTP-based communication, bidirectional streaming, better error handling
- Endpoint:
http://host:port/mcp - Recommended: This is the preferred transport for new deployments
Installation
# Clone or download this repository
cd ~/Desktop/APIWeaver
# Install dependencies
pip install -r requirements.txt
Usage
Claude Desktop
{
"mcpServers": {
"apiweaver": {
"command": "uvx",
"args": ["apiweaver", "run"]
}
}
}
Starting the Server
There are several ways to run the APIWeaver server with different transport types:
1. After installation (recommended):
If you have installed the package (e.g., using pip install . from the project root after installing requirements):
# Default STDIO transport
apiweaver run
# Streamable HTTP transport (recommended for web deployments)
apiweaver run --transport streamable-http --host 127.0.0.1 --port 8000
# SSE transport (legacy compatibility)
apiweaver run --transport sse --host 127.0.0.1 --port 8000
2. Directly from the repository (for development):
# From the root of the repository
python -m apiweaver.cli run [OPTIONS]
Transport Options:
--transport: Choose fromstdio(default),sse, orstreamable-http--host: Host address for HTTP transports (default: 127.0.0.1)--port: Port for HTTP transports (default: 8000)--path: URL path for MCP endpoint (default: /mcp)
Run apiweaver run --help for all available options.
Using with AI Assistants (like Claude Desktop)
APIWeaver is designed to expose web APIs as tools for AI assistants that support the Model Context Protocol (MCP). Here's how to use it:
-
Start the APIWeaver Server:
For modern MCP clients (recommended):
apiweaver run --transport streamable-http --host 127.0.0.1 --port 8000For legacy compatibility:
apiweaver run --transport sse --host 127.0.0.1 --port 8000For local desktop applications:
apiweaver run # Uses STDIO transport -
Configure Your AI Assistant: The MCP endpoint will be available at:
- Streamable HTTP:
http://127.0.0.1:8000/mcp - SSE:
http://127.0.0.1:8000/mcp - STDIO: Direct process communication
- Streamable HTTP:
-
Register APIs and Use Tools: Once connected, use the built-in
register_apitool to define web APIs, then use the generated endpoint tools.
Core Tools
The server provides these built-in tools:
- register_api - Register a new API and create tools for its endpoints
- list_apis - List all registered APIs and their endpoints
- unregister_api - Remove an API and its tools
- test_api_connection - Test connectivity to a registered API
- call_api - Generic tool to call any registered API endpoint
- get_api_schema - Get schema information for APIs and endpoints
API Configuration Format
{
"name": "my_api",
"base_url": "https://api.example.com",
"description": "Example API integration",
"auth": {
"type": "bearer",
"bearer_token": "your-token-here"
},
"headers": {
"Accept": "application/json"
},
"endpoints": [
{
"name": "list_users",
"description": "Get all users",
"method": "GET",
"path": "/users",
"params": [
{
"name": "limit",
"type": "integer",
"location": "query",
"required": false,
"default": 10,
"description": "Number of users to return"
}
]
}
]
}
Examples
Example 1: OpenWeatherMap API
{
"name": "weather",
"base_url": "https://api.openweathermap.org/data/2.5",
"description": "OpenWeatherMap API",
"auth": {
"type": "api_key",
"api_key": "your-api-key",
"api_key_param": "appid"
},
"endpoints": [
{
"name": "get_current_weather",
"description": "Get current weather for a city",
"method": "GET",
"path": "/weather",
"params": [
{
"name": "q",
"type": "string",
"location": "query",
"required": true,
"description": "City name"
},
{
"name": "units",
"type": "string",
"location": "query",
"required": false,
"default": "metric",
"enum": ["metric", "imperial", "kelvin"]
}
]
}
]
}
Example 2: GitHub API
{
"name": "github",
"base_url": "https://api.github.com",
"description": "GitHub REST API",
"auth": {
"type": "bearer",
"bearer_token": "ghp_your_token_here"
},
"headers": {
"Accept": "application/vnd.github.v3+json"
},
"endpoints": [
{
"name": "get_user",
"description": "Get a GitHub user's information",
"method": "GET",
"path": "/users/{username}",
"params": [
{
"name": "username",
"type": "string",
"location": "path",
"required": true,
"description": "GitHub username"
}
]
}
]
}
Authentication Types
Bearer Token
{
"auth": {
"type": "bearer",
"bearer_token": "your-token-here"
}
}
API Key (Header)
{
"auth": {
"type": "api_key",
"api_key": "your-key-here",
"api_key_header": "X-API-Key"
}
}
API Key (Query Parameter)
{
"auth": {
"type": "api_key",
"api_key": "your-key-here",
"api_key_param": "api_key"
}
}
Basic Authentication
{
"auth": {
"type": "basic",
"username": "your-username",
"password": "your-password"
}
}
Custom Headers
{
"auth": {
"type": "custom",
"custom_headers": {
"X-Custom-Auth": "custom-value",
"X-Client-ID": "client-123"
}
}
}
Parameter Locations
- query: Query string parameters (
?param=value) - path: Path parameters (
/users/{id}) - header: HTTP headers
- body: Request body (for POST, PUT, PATCH)
Parameter Types
- string: Text values
- integer: Whole numbers
- number: Decimal numbers
- boolean: true/false
- array: Lists of values
- object: JSON objects
Advanced Features
Custom Timeouts
{
"timeout": 60.0 // Timeout in seconds
}
Enum Values
{
"name": "status",
"type": "string",
"enum": ["active", "inactive", "pending"]
}
Default Values
{
"name": "page",
"type": "integer",
"default": 1
}
Claude Desktop Configuration
For Streamable HTTP Transport (Recommended)
{
"mcpServers": {
"apiweaver": {
"command": "apiweaver",
"args": ["run", "--transport", "streamable-http", "--host", "127.0.0.1", "--port", "8000"]
}
}
}
For STDIO Transport (Traditional)
{
"mcpServers": {
"apiweaver": {
"command": "apiweaver",
"args": ["run"]
}
}
}
Error Handling
The server provides detailed error messages for:
- Missing required parameters
- HTTP errors (with status codes)
- Connection failures
- Authentication errors
- Invalid configurations
Tips
- Choose the Right Transport: Use
streamable-httpfor modern deployments,stdiofor local tools - Test First: Always use
test_api_connectionafter registering an API - Start Simple: Begin with GET endpoints before moving to complex POST requests
- Check Auth: Ensure your authentication credentials are correct
- Use Descriptions: Provide clear descriptions for better AI understanding
- Handle Errors: The server will report HTTP errors with details
Troubleshooting
Common Issues
- 401 Unauthorized: Check your authentication credentials
- 404 Not Found: Verify the base URL and endpoint paths
- Timeout Errors: Increase the timeout value for slow APIs
- SSL Errors: Some APIs may require specific SSL configurations
Debug Mode
Run with ver
README truncated. View full README on GitHub.
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