mcpx

mcpx is a Python-based MCP server that provides a model context protocol implementation. It exposes a core module with a run() function, illustrating how to execute the server's context-aware tooling and fetch results. To use it, install the package dependencies, then run the module as a Python package to start the MCP server interface. The example in the README shows interacting with the core.run() function, which demonstrates the typical entry point for obtaining a model-context-driven result from the server. This server is designed to be lightweight and easily integrated into Python workflows that require standardized context handling.

Once running, you can import the server's core module in your Python project and call core.run() (or a similar entry point) to generate a model-context aware response. This makes mcpx suitable for experiments, prototyping, or embedding within larger AI tooling pipelines where consistent context management and tool invocation are important. If you need to customize behavior, refer to the mcpx.core API (e.g., different functions or configuration hooks) and adapt your code to supply prompts, tool invocations, or context data as needed.

Prerequisites:

• Python 3.8+ (or another supported Python version specified by mcpx)
• pip (comes with Python installations)

Step-by-step installation:

1. Clone or download the mcpx repository.
2. Create a virtual environment (recommended):

python3 -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Verify installation by running a quick test (as per usage example):

python - <<'PY'
from mcpx import core
print(core.run())
PY

Tips and common considerations:

• Ensure you are using a Python version compatible with mcpx and its dependencies. Virtual environments help avoid dependency conflicts.
• If you modify dependencies, re-run the installation step to update packages from requirements.txt.
• The example usage shows core.run(); depending on the version, you may have additional API options or configuration hooks. Check the project's API docs or source for advanced usage.
• When deploying, consider pinning exact package versions in requirements.txt to guarantee reproducible results.