MCP Server

The MCP Server provides a comprehensive framework for AI-powered command execution, plugin-based tools, and advanced features including synchronous script execution, secure Python evaluation, and knowledge management. It can be run as a standalone service or embedded in other projects to expose a consistent API for invoking tools and managing tasks.

Project Structure

• mcp_tools/ – Plugin framework and built-in tools with enhanced concurrency management
• server/ – Starlette server implementation with HTTP/SSE endpoints and DataFrame UI
• plugins/ – Extensible plugins (Azure DevOps, Git tools, knowledge indexing, Kusto, CircleCI)
• config/ – Environment manager and configuration helpers
• utils/ – Core utilities (async jobs, graph interface, memory management, vector store, PyEval)
• scripts/ – Installation, utility scripts, and automated submission workflows
• assets/ – Images and documentation resources

Docker

A Dockerfile is included for running the server in a container. Build the image with:

docker build -t mcp-server .

Then start the container exposing port 8000:

docker run -p 8000:8000 mcp-server

See docs/docker.md for more details.

Tool System

Tools are modular plugins registered through mcp_tools. Built-in utilities include:

• Command Executor – Synchronous and asynchronous command execution with run-to-completion support
• Browser Automation – Playwright integration for web interaction
• PyEval – Secure Python expression evaluation using RestrictedPython
• DataFrame Service – Data analysis and visualization with web interface
• Time Helpers – Time-based utilities and scheduling
• YAML Tool Loader – Dynamic tool definitions from YAML files

Additional plugins in the plugins/ directory include Azure DevOps integration, Git operations, knowledge indexing, Kusto queries, and CircleCI workflows. See mcp_tools/docs/creating_tools.md for details on building custom tools.

The web interface offers comprehensive dashboards:

• /tools – Browse all registered tools and view their details
• /dataframes – Interactive DataFrame management and visualization
• /knowledge – Knowledge graph exploration and management
• /pyeval – Secure Python evaluation interface

Plugin Management

External plugins can be installed by declaring them in plugin_config.yaml. Each entry should specify a plugin_repo in the form owner/repository and an optional sub_dir if the plugin lives in a subfolder. Example:

plugins:
   - plugin_repo: "github_owner/repo"
     sub_dir: "path/to/plugin"
     type: "python"

Run the mcp_admin tool with the refresh_plugins operation to clone or update plugins based on this configuration. Pass force=true to remove all installed plugins before reinstalling.

Key Features (v0.2.0)

• Run-to-Completion Execution – Synchronous script execution with comprehensive output capture
• PyEval Security – Safe Python expression evaluation using RestrictedPython
• Enhanced DataFrame UI – Responsive web interface for data analysis and visualization

Where to Go Next

1. Browse the documentation under mcp_tools/docs/ and docs/ to learn about tool creation, dependency injection, and advanced features.
2. Review the sample configuration files in server/ and try adding your own tools.
3. Explore plugins in the plugins/ directory for concrete implementations.
4. Check the utils/ directory for advanced utilities like vector stores, graph interfaces, and memory management.
5. See the CHANGELOG.md for detailed release notes and recent updates.

Editor Integration

Editors like Cursor/VSCode can use the SSE endpoint by adding the following to your settings:

{
  "mcpServers": {
    "mymcp-sse": { "url": "http://0.0.0.0:8000/sse" }
  }
}

Demo Screenshots

Installation

The project uses uv for dependency management. Install dependencies with:

uv sync

Or install in development mode using pip:

pip install -e .

Environment Setup

Configuration is controlled by .env files. Create one from the template and edit it with your settings:

cp config/env.template .env

Important variables include repository paths (GIT_ROOT), Azure Repo details (AZREPO_ORG, AZREPO_PROJECT, AZREPO_REPO), and optional PRIVATE_TOOL_ROOT for external tool configuration. The environment manager automatically loads .env files from the repository root, current directory, and your home directory.

Access settings in code via:

from config import env_manager
env_manager.load()
root = env_manager.get_git_root()

See docs/config_overview.md for more information.

Running the Server

After installing dependencies and configuring .env, start the server with:

uv run server/main.py

Connect to the SSE endpoint at http://0.0.0.0:8000/sse or use the additional routes in server/api.py. Background job endpoints are documented in docs/background_jobs_api.md.

Configuration Files

The server loads prompts and tool definitions from YAML files:

• server/prompts.yaml
• server/tools.yaml

Private overrides can be placed in server/.private/ or in a folder pointed to by PRIVATE_TOOL_ROOT. Files are resolved in this order:

1. PRIVATE_TOOL_ROOT
2. server/.private/
3. Defaults in server/

Running Tests

Execute all test suites with:

scripts/run_tests.sh

Or run pytest directly on mcp_tools/tests, plugins/*/tests, or server/tests.