mcp-http-bridge 0.1.0

Bridge for stdio-only MCP tools that exposes their stdio protocol via streamable-http

MCP Network Bridge

Enable network access to STDIO-based MCP servers through HTTP streamable protocol.

The Problem

Many open-source MCP servers only support the local-only stdio protocol. For one reason or another their developers never bothered to implement one of the two network-enabled modes: SSE or StreamableHTTP. In most cases that's fine - Claude Code or Cursor or any other modern IDE supports MCP over stdio. But when your AI application is not a stock-standard IDE that needs access to some MCP server over the network you've got a problem.

One option is to add Streaming HTTP support to the MCP servers that you want to use. It's usually not very difficult but also it's not very scalable.

A better option is to Bridge the STDIO local-only protocol to StreamableHTTP network-enabled protocol. And that's what this project is for.

Learn more about STDIO and StreamableHTTP MCP Protocols

Quick Start

The typical MCP server will provide a sample config to use. It could be something like:

{
  "server": {
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-everything"
    ]
  }
}

We can use the same config.json with mcp-network-bridge:

Run with Docker:

docker run --rm -it -p 8000:8000 -v $(pwd)/config.json:/app/config.json ghcr.io/mludvig/mcp-http-bridge

Or use docker-compose:

docker-compose up

Your MCP server is now available at http://localhost:8000/mcp/. Test it out with MCP Inspector.

MCP server network access

With the bridge running the MCP server can now be accessed over the network:

{
  "server": {
    "type": "http",
    "url": "http://127.0.0.1:8000/mcp/",
  }
}

Security

This bridge doesn't implement any security or access control. You'll have to configure something like nginx in front of it for TLS or Authentication support. That's beyond the scope of this project since better and more appropriate tools already exist.

Development

Local Development

This project uses uv for dependency management:

# Install dependencies
uv sync --extra dev

# Run tests
uv run pytest

# Run linting
uv run ruff check src/ tests/

# Format code
uv run ruff format src/ tests/

# Run the CLI
uv run mcp-http-bridge --help

CI/CD

This project uses GitHub Actions for:

• Continuous Integration: Runs tests, linting, and formatting checks on every push and PR
• Build and Release: Automatically builds and publishes to PyPI and GHCR when tags are pushed
• Dependency Updates: Dependabot keeps dependencies up to date

To publish a new release:

1. Update the version in pyproject.toml
2. Create a git tag: git tag v0.1.1
3. Push the tag: git push origin v0.1.1
4. GitHub Actions will automatically build and publish to PyPI and GHCR

Publishing to PyPI

The workflow uses Trusted Publishing to securely publish to PyPI without storing API tokens. Make sure to configure the PyPI trusted publisher for this repository.

Author

Michael Ludvig