turbomcp 0.2.0

Zero-dependency tool registry and MCP server for LLM agents, with built-in tool search.

turbomcp

A zero-dependency tool registry and MCP server for LLM agents — with built-in tool search so agents don't drown in schemas.

Write plain Python functions. Get JSON Schema, OpenAI function-calling definitions, a real MCP server, and ranked tool search — without installing anything else.

from turbomcp import ContextAware

mcp = ContextAware()

@mcp.tool()
def greet(name: str = "World"):
    """Greet a user by name.

    Args:
        name (str): Who to greet
    """
    return f"Hello, {name}!"

That's a working tool. Schemas are generated from the signature and docstring — types from annotations, descriptions from the Args: section, required/optional from defaults.

Why turbomcp?

Most tool servers dump every tool schema into the model's context on every request. That's fine for 5 tools and a disaster for 50. turbomcp is built around progressive disclosure — an agent can work its way down this ladder and only pay for what it needs:

Call	Returns	Context cost
mcp.list_tools()	just the names	tiny
mcp.list_tools(with_summary=True)	names + one-liners	small
mcp.search_tools("fetch a webpage")	ranked matches, full schemas	only what's relevant
mcp.get_capabilities(name="read_file")	one full schema on demand	one tool

Search is keyword-based out of the box (name/description/parameter matching with field weighting), and optionally semantic via static embeddings — no GPU, ~1s startup, instant queries.

Install

pip install turbomcp              # core: registry + MCP server, zero dependencies
pip install turbomcp[web]         # + built-in web tools (search, scrape)
pip install turbomcp[semantic]    # + semantic tool search (model2vec)
pip install turbomcp[http]        # + Flask REST API for debugging
pip install turbomcp[all]         # everything

Use it as an MCP server

Any MCP client (Claude Code, Claude Desktop, ...) can launch your registry directly:

turbomcp serve my_tools.py          # finds the ContextAware instance in the file
turbomcp serve my_tools.py:mcp     # or name the variable explicitly
turbomcp demo                       # try it with the built-in default tools
turbomcp init [my_tools.py]         # create a starter template
turbomcp init my_tools.py --with-web --with-semantic  # include optional features
turbomcp help                       # show all commands

Claude Code:

claude mcp add my-tools -- turbomcp serve /path/to/my_tools.py

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "my-tools": {
      "command": "turbomcp",
      "args": ["serve", "/path/to/my_tools.py"]
    }
  }
}

The server implements MCP over stdio natively (JSON-RPC 2.0): initialize, ping, tools/list, tools/call, and it emits notifications/tools/list_changed when tools are registered or removed at runtime — register a new tool while the server is live and connected clients find out immediately.

One rule when serving stdio: don't print() to stdout in your tool file — it corrupts the protocol stream. Use sys.stderr.

Lazy mode: tool search over MCP

turbomcp serve my_tools.py --lazy

In lazy mode the client doesn't see your catalog at all. It sees three meta-tools — search_tools, get_tool, call_tool — and discovers what it needs by searching. A 100-tool registry costs the model three schemas instead of a hundred.

Use it with OpenAI-compatible APIs

The same registry plugs straight into any chat-completions API (OpenAI, Gemini's OpenAI endpoint, Ollama, llama.cpp, vLLM, ...):

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="unused")

response = client.chat.completions.create(
    model="your-model",
    messages=[{"role": "user", "content": "Greet Ada"}],
    tools=mcp.to_openai_tools(),          # <- emitted in OpenAI format
)

for call in response.choices[0].message.tool_calls or []:
    import json
    result = mcp.call(call.function.name, json.loads(call.function.arguments))

Also available: mcp.to_mcp_tools() (MCP format) and mcp.get_tools_prompt() (plain text for models without native tool calling).

Tool search

mcp.search_tools("fetch a webpage", top_n=3)
# {"query": ..., "semantic": False, "tools": [{"name": "webpage_scrape", "score": ..., ...}]}

Enable semantic search to close the synonym gap ("say hello" → greet):

mcp.enable_semantic_search()   # pip install turbomcp[semantic]

Keyword and semantic scores are blended (tunable alpha); without the extra installed, search falls back to pure keyword scoring automatically.

Built-in default tools

mcp.register_default(all=True)                              # everything
mcp.register_default(names=["calculate", "read_file"])      # or pick

Tool	Needs [web] extra
internet_search — DuckDuckGo (default) or Google, text or images	yes
webpage_scrape — fetch a page, strip boilerplate, return text	yes
get_current_time — local/IANA-timezone/UTC time	no
read_file — read a local text file	no
calculate — math expressions via a safe AST evaluator (no eval)	no
get_system_info — OS, arch, hostname, Python version	no

Writing good tools

Schemas are only as good as your signatures and docstrings:

from typing import Literal, Optional

@mcp.tool()
def convert(path: str, format: Literal["mp4", "webm", "gif"], quality: Optional[int] = None):
    """Convert a video file to another format.

    Args:
        path (str): Path to the input file
        format (str): Output format
        quality (int): Output quality 1-100, default keeps source quality
    """
    ...

• Annotations → types: str, int, float, bool, list[str], dict, Optional[X], Literal[...] (becomes an enum) all map to proper JSON Schema.
• Defaults → optional: parameters without defaults are required.
• Args: section → descriptions: Google-style docstrings, one line per parameter.
• The first docstring paragraph becomes the tool description — make it say when to use the tool, not just what it does.

Optional HTTP API

For curl-debugging or non-MCP clients (pip install turbomcp[http]):

mcp.start_server(port=5000)   # generates an API token, printed to stderr

Endpoint	Description
GET /tools?summary=true	names (+ summaries)
GET /capabilities	full catalog
GET /tool/<name>	one tool's schema
GET /search?q=...	ranked search
POST /call_tool	execute — {"name": ..., "params": {...}}, token via X-API-Key header or api_key field

The server binds 127.0.0.1 and only /call_tool requires the token. It's a Flask dev server: fine on localhost, not meant for the open internet.

Security notes

• Tools run with your user's permissions. A tool like "run shell command" gives the model exactly that power — register it knowingly.
• calculate uses an AST walker, not eval — sandbox-escape expressions like (1).__class__... are rejected.
• The HTTP token is generated per-process and printed to stderr; MCP stdio needs no token because the client launches the process itself.

License

MIT