What dependencies does MCPLite require?
Core dependencies are `pydantic` and `rich`. For web functionality, install `requests`, `beautifulsoup4`, and `markdownify`. The `chain-of-thought` package is recommended for stdio transport.
@acesanderson
Basic information
MCP client/server framework from scratch
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MCPLite": {
"command": "python",
"args": [
"my_server.py"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCPLite is a lightweight, pythonic implementation of the Model Context Protocol (MCP) for integrating external tools and data sources into LLM applications. It provides a decorator-based API inspired by FastAPI to turn any Python function into an MCP tool, resource, or prompt. It is designed for Python developers building MCP servers or connecting LLMs to external capabilities.
Install core dependencies (pydantic, rich) and optional ones for web functionality. Create an MCP server by instantiating MCPLite(transport="stdio") and decorating functions with @mcp.tool, @mcp.resource, or @mcp.prompt. Run the server with mcp.run(). For client orchestration, use the Host class; for interactive chat, use MCPChat. Built-in servers (e.g., fetch, obsidian) can be started via python -m MCPLite.servers.<name>.
Core dependencies are `pydantic` and `rich`. For web functionality, install `requests`, `beautifulsoup4`, and `markdownify`. The `chain-of-thought` package is recommended for stdio transport.
MCPLite supports stdio (production), direct (development), and SSE (web) transports. You specify the transport when creating the server: `MCPLite(transport="stdio")`.
Set the `OBSIDIAN_PATH` environment variable to your vault’s path. The server then runs sandboxed filesystem operations, searches, and metadata extraction within that vault.
Yes. Create a new Python file, instantiate `MCPLite`, and use the decorator pattern (`@mcp.tool`, etc.) to add tools, resources, or prompts. The server can then be run or imported.
Logging is configurable via the `MCPLITE_LOG_LEVEL` environment variable (e.g., `DEBUG`). By default, it logs at the configured level to standard output.
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