GraphCanon updated 3w · GitHub synced 3w
Decision brief
MCP-Bridge facilitates integration of MCP tools with systems expecting an OpenAI API interface via Python.
Good fit when
- 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
Avoid when
- 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
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (230d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install MCP-BridgeSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
SecretiveShell/MCP-Bridge is a Python-based middleware project that enables interaction with Model Context Protocol (MCP) tools via an OpenAI-compatible API endpoint.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 27, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 27, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 27, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 27, 2026
Categories
Tags
README
Installation
The 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.
Note that this requires an inference engine with tool call support. I have tested this with vLLM with success, though ollama should also be compatible.
Docker installation
-
Clone the repository
-
Edit the compose.yml file
You will need to add a reference to the config.json file in the compose.yml file. Pick any of
- 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)
- add a http url to the environment variables to download the config.json file from a url
- add the config json directly as an environment variable
see below for an example of each option:
environment:
- MCP_BRIDGE__CONFIG__FILE=config.json # mount the config file for this to work
- MCP_BRIDGE__CONFIG__HTTP_URL=http://10.88.100.170:8888/config.json
- MCP_BRIDGE__CONFIG__JSON={"inference_server":{"base_url":"http://example.com/v1","api_key":"None"},"mcp_servers":{"fetch":{"command":"uvx","args":["mcp-server-fetch"]}}}
The mount point for using the config file would look like:
volumes:
- ./config.json:/mcp_bridge/config.json
- run the service
docker-compose up --build -d
Manual installation (no docker)
If you want to run the application without docker, you will need to install the requirements and run the application manually.
-
Clone the repository
-
Set up a dependencies:
uv sync
- Create a config.json file in the root directory
Here is an example config.json file:
{
"inference_server": {
"base_url": "http://example.com/v1",
"api_key": "None"
},
"mcp_servers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}
- Run the application:
uv run mcp_bridge/main.py
License
MCP-Bridge is licensed under the MIT License. See the LICENSE file for more information.
For agents
This page has a .md twin and JSON over the API.