mcp

# 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 ## Installation The project uses `uv` for dependency management. Install dependencies with: ```bash uv sync ``` Or install in development mode using pip: ```bash pip install -e . ``` ## Environment Setup Configuration is controlled by `.env` files. Create one from the template and edit it with your settings: ```bash 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: ```python 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: ```bash 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`. ## Docker A `Dockerfile` is included for running the server in a container. Build the image with: ```bash docker build -t mcp-server . ``` Then start the container exposing port `8000`: ```bash docker run -p 8000:8000 mcp-server ``` See `docs/docker.md` for more details. ## 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/` ## 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: ```yaml 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. ## Running Tests Execute all test suites with: ```bash scripts/run_tests.sh ``` Or run `pytest` directly on `mcp_tools/tests`, `plugins/*/tests`, or `server/tests`. ## 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: ```json { "mcpServers": { "mymcp-sse": { "url": "http://0.0.0.0:8000/sse" } } } ``` ## Demo Screenshots