The BioContext AI meta mcp enables access to all installable MCP servers in the BioContextAI registry with minimal context consumption.
Project description
BioContext AI Meta MCP
The BioContext AI Meta MCP enables access to all installable MCP servers in the BioContextAI registry with minimal context consumption.
Getting started
Please refer to the documentation, in particular, the API documentation.
You can also find the project on BioContextAI, the community-hub for biomedical MCP servers: meta-mcp on BioContextAI.
Installation
You need to have Python 3.11 or newer installed on your system.
If you don't have Python installed, we recommend installing uv. Internally we also make use of an LLM to generate structured tools calls, so you need to provide an API key for your chosen provider (OpenAI, Anthropic, or Google) as described below. The model can be changed by setting the META_MCP_MODEL environment variable or the --model flag, e.g., to openai/gpt-5-nano or anthropic/claude-haiku-4-5-20251001. We recommend using openai/gpt-5-nano or openai/gpt-5-mini for their guaranteed structured output support.
There are several alternative options to install meta-mcp:
1. Use uvx to run it immediately
After publication to PyPI:
uvx biocontext-meta
Or from a Git repository:
uvx git+https://github.com/biocontext-ai/meta-mcp.git@main
2. Include it in one of various clients that supports the mcp.json standard
If your MCP server is published to PyPI, use the following configuration:
{
"mcpServers": {
"meta-mcp": {
"command": "uvx",
"args": ["biocontext-meta"],
"env": {
"OPENAI_API_KEY": "YOUR OPENAI_API_KEY",
"ANTHROPIC_API_KEY": "YOUR ANTHROPIC_API_KEY",
"GEMINI_API_KEY": "YOUR GEMINI_API_KEY"
}
}
}
}
In case the MCP server is not yet published to PyPI, use this configuration:
{
"mcpServers": {
"meta-mcp": {
"command": "uvx",
"args": ["git+https://github.com/biocontext-ai/meta-mcp.git@main"],
"env": {
"OPENAI_API_KEY": "YOUR OPENAI_API_KEY",
"ANTHROPIC_API_KEY": "YOUR ANTHROPIC_API_KEY",
"GEMINI_API_KEY": "YOUR GEMINI_API_KEY"
}
}
}
}
For purely local development (e.g., in Cursor or VS Code), use the following configuration (you can also provide API keys in an .env file):
{
"mcpServers": {
"meta-mcp": {
"command": "uvx",
"args": [
"--refresh",
"--from",
"path/to/repository",
"biocontext-meta"
],
"env": {
"OPENAI_API_KEY": "YOUR OPENAI_API_KEY",
"ANTHROPIC_API_KEY": "YOUR ANTHROPIC_API_KEY",
"GEMINI_API_KEY": "YOUR GEMINI_API_KEY"
}
}
}
}
If you want to reuse an existing environment for local development, use the following configuration (you can also provide API keys in an .env file):
{
"mcpServers": {
"meta-mcp": {
"command": "uv",
"args": ["run", "--directory", "path/to/repository", "biocontext-meta"],
"env": {
"OPENAI_API_KEY": "YOUR OPENAI_API_KEY",
"ANTHROPIC_API_KEY": "YOUR ANTHROPIC_API_KEY",
"GEMINI_API_KEY": "YOUR GEMINI_API_KEY"
}
}
}
}
3. Install it through pip
pip install --user biocontext-meta
4. Install the latest development version
pip install git+https://github.com/biocontext-ai/meta-mcp.git@main
How it works
The BioContext AI Meta MCP provides dynamic access to MCP servers from the BioContextAI registry with minimal context consumption. It works through several key mechanisms:
- Dynamic server connections: Automatically connects to and manages MCP servers on-demand, loading configurations and tool metadata from remote JSON registries
- LLM-powered search: Uses AI to intelligently search and filter available servers and tools across multiple modes (string matching, semantic search, and LLM-based reasoning)
- Structured output generation: Leverages LiteLLM integration to generate properly structured tool calls with JSON schema validation and Pydantic model generation
- Tool exploration: Provides dynamic discovery and exploration of available tools with configurable result limits and comprehensive metadata access
Known Issues
- When using the
--connect-on-startupflag, the server might have trouble starting, depending on the client
Contact
If you found a bug, please use the issue tracker.
Citation
t.b.a
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