Generalized MCP Server
Provides tools for interacting with GitHub's API, enabling management of repositories, issues, pull requests, and other GitHub resources programmatically through the PyGithub SDK.
Enables management and interaction with Kubernetes clusters, providing access to pods, services, deployments, and other Kubernetes resources through the official Kubernetes Python client.
Integrates with OpenAI's API for calling language models, with configurable model selection and API key authentication for AI-powered operations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Generalized MCP Serverlist all pods in the default namespace"
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.
Generalized MCP Server
This project implements a generalized Model Control Plane (MCP) server that dynamically exposes the full surface area of arbitrary Python SDKs via an agent‐friendly gRPC interface.
Current reference adapters:
Kubernetes –
kubernetes-client/pythonGitHub –
PyGithubAzure – selected services from
azure-sdk-for-python
Quick Start
# Clone & enter repo
git clone <repo-url> generalized_mcp && cd generalized_mcp
# Create virtualenv
python -m venv .venv && source .venv/bin/activate
# Install deps
pip install -r requirements.txt
# Generate gRPC stubs (once per proto change)
make proto
# Run MCP server (listens on :50051 by default)
python -m src.serverEnvironment Variables
Variable | Description |
| API key for calling OpenAI models |
| (optional) Model name, defaults to |
| Path to kubeconfig for Kubernetes adapter |
| PAT for GitHub adapter |
| Credentials for Azure SDK |
Roadmap
Reflective adapter loading for arbitrary SDKs
Streaming function call extraction via LLM
Full MCP compliance tests
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