How do I use OpenAPI MCP Bridge?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@OpenAPI MCP Bridge search for pet endpoints in petstore API" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

OpenAPI MCP Bridge

by samuellawrence

Overview Schema Related Servers Score Discussions

Python

Remote

OpenAPI MCP Bridge

A generic MCP server that takes any OpenAPI/Swagger specification and exposes it as MCP tools, allowing AI assistants to search, explore, and execute any REST API.

Architecture

                                    +------------------+
                                    |   Claude / AI    |
                                    +--------+---------+
                                             |
                                             | MCP Protocol
                                             v
+------------------------------------------------------------------+
|                      OpenAPI MCP Bridge                          |
|                                                                  |
|  +-------------+    +-------------+    +------------------+      |
|  |   Config    |    |   Parser    |    |    Registry      |      |
|  | (apis.json) |--->| (OpenAPI)   |--->| (API endpoints)  |      |
|  +-------------+    +-------------+    +------------------+      |
|                                                 |                |
|  +-------------+    +-------------+    +--------v---------+      |
|  | Guardrails  |    |   Search    |    |    Executor      |      |
|  | (safety)    |    |  (fuzzy)    |    |    (httpx)       |      |
|  +-------------+    +-------------+    +------------------+      |
|                                                 |                |
+------------------------------------------------------------------+
                                                  |
                                                  v
                                    +------------------+
                                    |    REST APIs     |
                                    | (Petstore, etc)  |
                                    +------------------+

Related MCP server: Swagger MCP

Quick Start

Installation

# Clone the repository
git clone https://github.com/your-username/openapi-mcp-bridge.git
cd openapi-mcp-bridge

# Install with uv
uv sync --all-extras

# Or with pip
pip install -e ".[dev]"

Register an API

Edit config/apis.json to add your API:

{
  "apis": [
    {
      "name": "my-api",
      "spec_url": "https://api.example.com/openapi.json",
      "base_url": "https://api.example.com",
      "auth": {
        "type": "bearer",
        "token": "$MY_API_TOKEN",
        "header_name": "Authorization"
      },
      "settings": {
        "default_page_size": 20,
        "confirm_destructive": true
      }
    }
  ]
}

Xquik API Example

For a real authenticated OpenAPI 3.1 API, copy config/xquik.apis.example.json to your active config and set XQUIK_API_KEY. The example registers Xquik's public spec for X/Twitter automation, trends, tweet search, media, monitor, and webhook endpoints.

Run the Server

# Run with fuzzy search (default)
python -m src.server

# Run with embedding search (better semantic understanding)
python -m src.server --search-provider embedding

# Or set via environment variable
SEARCH_PROVIDER=embedding python -m src.server

Install Embedding Search (Optional)

For better semantic search using sentence-transformers:

# With uv
uv pip install "sentence-transformers>=2.2.0" "numpy>=1.24.0"

# Or with pip
pip install sentence-transformers numpy

Configuration Reference

API Configuration

Field	Type	Required	Description
`name`	string	Yes	Unique identifier for the API
`spec_url`	string	Yes	URL or local path to OpenAPI spec
`base_url`	string	Yes	Base URL for API requests
`auth`	object	No	Authentication configuration
`settings`	object	No	API-specific settings

Authentication Types

Type	Description	Example
`bearer`	Bearer token in Authorization header	`Authorization: Bearer <token>`
`api_key`	API key in header or query param	`X-API-Key: <token>`
`basic`	Basic auth (base64 encoded)	`Authorization: Basic <base64>`
`none`	No authentication	-

Environment variables can be referenced with $VAR_NAME syntax.

Settings

Setting	Default	Description
`default_page_size`	20	Default limit for list responses
`max_batch_size`	50	Maximum requests in batch execution
`rate_limit_per_second`	5	Rate limiting for requests
`confirm_destructive`	true	Require confirmation for DELETE/PUT/PATCH

Search Providers

Fuzzy Search (Default)

Uses thefuzz library for string matching. Fast and works without additional dependencies.

python -m src.server --search-provider fuzzy

Best for: Exact keyword matching, operation IDs, path names.

Embedding Search

Uses sentence-transformers for semantic similarity. Better understanding of synonyms and intent.

python -m src.server --search-provider embedding

Best for: Natural language queries, synonym matching, semantic similarity.

Comparison:

Query	Fuzzy	Embedding
"find all pets"	High confidence	High confidence
"what animals are available"	Low confidence	High confidence
"buy a pet"	Mixed results	Finds store/order
"remove a specific animal"	Mixed results	Finds DELETE pet

Tool Reference

1. list_apis

List all registered OpenAPI/Swagger APIs.

Input: None

Output:

[
  {
    "name": "petstore",
    "base_url": "https://petstore.example.com",
    "description": "Pet Store API",
    "auth_type": "api_key",
    "endpoint_count": 15
  }
]

2. search_endpoints

Search for API endpoints by natural language description.

Input:

{
  "api": "petstore",
  "query": "find all available pets",
  "limit": 5
}

Output:

{
  "api": "petstore",
  "query": "find all available pets",
  "results": [
    {
      "endpoint": {
        "path": "/pet/findByStatus",
        "method": "GET",
        "summary": "Finds Pets by status",
        "parameters": [...]
      },
      "similarity_score": 0.85,
      "low_confidence": false
    }
  ],
  "total_results": 5
}

3. execute_endpoint

Execute an API endpoint.

Input:

{
  "api": "petstore",
  "path": "/pet/{petId}",
  "method": "GET",
  "params": {"petId": 123},
  "limit": 20,
  "offset": 0,
  "confirmed": false
}

Output:

{
  "status_code": 200,
  "data": {"id": 123, "name": "Buddy", "status": "available"},
  "truncated": false,
  "auth_error": false
}

For destructive operations (DELETE, PUT, PATCH), set confirmed: true.

4. batch_execute

Execute multiple API endpoints in parallel.

Input:

{
  "api": "petstore",
  "requests": [
    {"path": "/pet/1", "method": "GET"},
    {"path": "/pet/2", "method": "GET"},
    {"path": "/pet/3", "method": "GET"}
  ],
  "parallel": true,
  "confirmed": true
}

Output:

{
  "results": [...],
  "summary": {"total": 3, "succeeded": 3, "failed": 0}
}

Usage Examples

Example 1: Simple Query

User: What pets are available in the store?

AI: Let me search for that endpoint and get the data.
    1. search_endpoints(api="petstore", query="find available pets")
    2. execute_endpoint(api="petstore", path="/pet/findByStatus",
                        method="GET", params={"status": "available"})

    Result: Found 6 available pets: Buddy (dog), Whiskers (cat), ...

Example 2: Creating a Resource

User: Add a new pet named "Max" to the store.

AI: 1. search_endpoints(api="petstore", query="create new pet")
    2. execute_endpoint(api="petstore", path="/pet", method="POST",
                        body={"name": "Max", "species": "dog", "status": "available"})

    Result: Created pet with ID 15.

Example 3: Destructive Operation

User: Delete pet #15.

AI: 1. execute_endpoint(api="petstore", path="/pet/{petId}", method="DELETE",
                        params={"petId": 15}, confirmed=false)

    Warning: This is a destructive operation. Set confirmed=true to proceed.

User: Yes, go ahead.

AI: 2. execute_endpoint(..., confirmed=true)

    Result: Pet #15 deleted successfully.

Claude Desktop Integration

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

{
  "mcpServers": {
    "openapi-bridge": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/openapi-mcp-bridge",
      "env": {
        "PETSTORE_KEY": "your-api-key"
      }
    }
  }
}

Claude Code Integration

Create .mcp.json in your project root:

{
  "mcpServers": {
    "openapi-bridge": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/openapi-mcp-bridge",
      "env": {
        "PETSTORE_KEY": "your-api-key"
      }
    }
  }
}

Testing

Run Unit Tests

pytest tests/test_parser.py tests/test_search.py tests/test_executor.py -v

Run Integration Tests

# Requires mock server to start automatically
pytest tests/test_integration.py -v

Start Mock Server Manually

cd mock-petstore
./run.sh

Development

Project Structure

openapi-mcp-bridge/
├── src/
│   ├── __init__.py
│   ├── server.py          # MCP server with 4 tools
│   ├── config.py          # Configuration loader
│   ├── parser.py          # OpenAPI spec parser
│   ├── registry.py        # API registry
│   ├── executor.py        # HTTP request executor
│   ├── guardrails.py      # Safety checks
│   └── search/
│       ├── base.py        # Search interface
│       ├── fuzzy.py       # Fuzzy search
│       └── embedding.py   # Embedding search (placeholder)
├── config/
│   └── apis.json          # API registrations
├── mock-petstore/         # Test server
├── tests/
└── pyproject.toml

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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