{"data":{"slug":"secretiveshell-mcp-bridge","name":"MCP-Bridge","tagline":"A middleware for an openAI compatible endpoint to call MCP tools","github_url":"https://github.com/SecretiveShell/MCP-Bridge","owner":"SecretiveShell","repo":"MCP-Bridge","owner_avatar_url":"https://avatars.githubusercontent.com/u/84923604?v=4","primary_language":"Python","stars":928,"forks":117,"topics":["ai","claude","mcp","mcp-server","mcp-servers","model-context-protocol","openai","openai-api"],"archived":false,"github_pushed_at":"2025-12-08T15:22:32+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/secretiveshell-mcp-bridge","markdown_url":"https://www.graphcanon.com/tools/secretiveshell-mcp-bridge.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/secretiveshell-mcp-bridge","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=secretiveshell-mcp-bridge","description":"A middleware to provide an openAI compatible endpoint that can call MCP tools","homepage_url":null,"license":"MIT","open_issues":36,"watchers":8,"ai_summary":"SecretiveShell/MCP-Bridge is a Python-based middleware project that enables interaction with Model Context Protocol (MCP) tools via an OpenAI-compatible API endpoint.","readme_excerpt":"## Installation\n\nThe recommended way to install MCP-Bridge is to use Docker. See the example compose.yml file for an example of how to set up docker. \n\nNote that this requires an inference engine with tool call support. I have tested this with vLLM with success, though ollama should also be compatible.\n\n---\n\n### Docker installation\n\n1. **Clone the repository**\n\n2. **Edit the compose.yml file**\n\nYou will need to add a reference to the config.json file in the compose.yml file. Pick any of\n- add the config.json file to the same directory as the compose.yml file and use a volume mount (you will need to add the volume manually)\n- add a http url to the environment variables to download the config.json file from a url\n- add the config json directly as an environment variable\n\nsee below for an example of each option:\n```bash\nenvironment:\n  - MCP_BRIDGE__CONFIG__FILE=config.json # mount the config file for this to work\n  - MCP_BRIDGE__CONFIG__HTTP_URL=http://10.88.100.170:8888/config.json\n  - MCP_BRIDGE__CONFIG__JSON={\"inference_server\":{\"base_url\":\"http://example.com/v1\",\"api_key\":\"None\"},\"mcp_servers\":{\"fetch\":{\"command\":\"uvx\",\"args\":[\"mcp-server-fetch\"]}}}\n```\nThe mount point for using the config file would look like:\n```yaml\n    volumes:\n      - ./config.json:/mcp_bridge/config.json\n```\n\n3. **run the service**\n```\ndocker-compose up --build -d\n```\n\n---\n\n### Manual installation (no docker)\n\nIf you want to run the application without docker, you will need to install the requirements and run the application manually.\n\n1. **Clone the repository**\n\n2. **Set up a dependencies:**\n```bash\nuv sync\n```\n\n3. **Create a config.json file in the root directory**\n\nHere is an example config.json file:\n```json\n{\n   \"inference_server\": {\n      \"base_url\": \"http://example.com/v1\",\n      \"api_key\": \"None\"\n   },\n   \"mcp_servers\": {\n      \"fetch\": {\n        \"command\": \"uvx\",\n        \"args\": [\"mcp-server-fetch\"]\n      }\n   }\n}\n```\n\n4. **Run the application:**\n```bash\nuv run mcp_bridge/main.py\n```\n\n---\n\n## License\nMCP-Bridge is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.","github_created_at":"2024-11-30T19:29:35+00:00","created_at":"2026-07-11T11:53:26.276814+00:00","updated_at":"2026-07-27T06:00:48.240852+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"claude","name":"claude"},{"slug":"mcp","name":"mcp"},{"slug":"model-context-protocol","name":"model-context-protocol"},{"slug":"openai","name":"openai"}],"trust":{"provenance":{"is_fork":false,"github_id":896594784,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-27T06:00:47.427Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":230,"last_release_at":"2025-02-08T22:32:33Z"},"security_summary":{"status":"no_manifest","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:53:28.200Z","medium_count":0,"scan_profile":"mcp_manifest","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-27T06:00:47.900Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-07-27T06:00:47.900Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-07-27T06:00:47.900Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-27T06:00:47.900Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-07-27T06:00:47.900Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-27T06:00:47.900Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When needing to interact with MCP tools through an OpenAI-compatible endpoint","To enable legacy systems expecting the OpenAI API to work with MCP services"],"when_not_to_use":["If your system can natively support and communicate directly with MCP protocols without API translation","For scenarios where OpenAI compatibility is not required, as using MCP-Bridge would introduce unnecessary complexity"],"source":"enrich:decision_facts","observed_at":"2026-07-17T02:12:39.181Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"MCP-Bridge facilitates integration of MCP tools with systems expecting an OpenAI API interface via Python."}]}}