| name | llm_mcp |
| description | MCP server that lets Claude Code (or any MCP client) discover and call local LLM servers (LM Studio, Ollama, …) as tools, to offload cheap tasks off the cloud. Use when the user wants to wire local models into their agent, register an MCP server, or have the agent automatically route simple tasks to local hardware. |
llm_mcp — Local LLM tools over Model Context Protocol
Exposes [[llm_backends]] as MCP tools so an agent can route work to local models
on its own. Pure stdlib, stdio JSON-RPC 2.0, no dependencies.
Tools exposed
| Tool | Purpose |
|---|
discover_backends | Scan localhost/network, register reachable LLM servers |
list_models | List models the registered backends expose |
audit_local_usage | Local-usage report + hardware-aware setup steps |
route_task | Recommend cheapest tier + a concrete local backend/model |
local_chat | Run a prompt on a local model (0 cloud tokens) |
Register in Claude Code
Add to .mcp.json at the project root (an example ships at
configs/mcp.example.json):
{
"mcpServers": {
"botte-llm": {
"command": "python",
"args": ["-m", "skills.llm_mcp.server"],
"cwd": "/absolute/path/to/botte-secrete"
}
}
}
Then in a session: "discover my local backends", "classify these 50 tickets
locally", "is local routing active?" — the agent calls the tools directly.
Lazy tool loading (default on)
The server has grown to ~39 tools; injecting every full JSON Schema into an
agent's context costs ~3.9k tokens on every turn, whether or not that turn
uses them — [[context_profiler]] measured it as the single biggest slice of
always-on prefix on this repo. So tools/list returns only a small core
(local_chat, auto_route, find_skills, conduct) plus a find_tool(query)
meta-tool — the same pattern this very harness uses (ToolSearch). Call
find_tool to discover anything else; a strong match returns the full schema in
one round trip. tools/call still dispatches any tool by name regardless of
what was listed — lazy loading only shrinks the catalog, not what's callable.
Measured saving: ~3.3k tokens (84% of the tool-schema cost). Disable with
BOTTE_MCP_LAZY_TOOLS=0 for a client that expects the full catalog upfront.
Manual smoke test
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{}}}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
'{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"local_chat","arguments":{"prompt":"ping","max_tokens":16}}}' \
| python -m skills.llm_mcp.server
Token strategy
Tell the agent: route classification / extraction / short summaries through
local_chat, keep architecture & security reasoning on the cloud. That single
rule typically moves 30-50% of calls to zero-cost local inference.
