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ollama-handoff
Advanced tools
An MCP server that offloads cheap work from your cloud LLM agent to a local Ollama model.
An MCP server that offloads cheap work from your cloud LLM agent to a local Ollama model.
Your frontier model (Claude, GPT, etc.) is brilliant and metered. A lot of the work it gets handed — summarizing a log, drafting a commit message, pulling every URL out of a file, a quick first-pass code review — doesn't need frontier reasoning at all. ollama-handoff exposes your local Ollama instance as a handful of purpose-built MCP tools, so your agent can route that work to a model on your own GPU — at zero cloud cost — and spend its (paid) reasoning budget on the things that actually need it.
This isn't a generic "wrap the Ollama API" server. Each tool ships with a baked-in system prompt and a description written for the calling agent, so the agent knows when to hand off and gets a tuned result back without re-stating instructions every call.
summarize_local, code_review_local, draft_commit_message_local, and extract_local come with reviewer/summarizer/extractor system prompts already dialed in.mcp, httpx), fully typed, unit-tested, no telemetry.ollama serve) with at least one model pulled, e.g. ollama pull qwen2.5-coder:14b.uvx, which manages it for you).The fastest path is uv — no manual venv needed:
uvx ollama-handoff # run directly
# or
pip install ollama-handoff # then run: ollama-handoff
claude mcp add ollama-handoff -- uvx ollama-handoff
mcp config block){
"mcpServers": {
"ollama-handoff": {
"command": "uvx",
"args": ["ollama-handoff"],
"env": {
"OLLAMA_DEFAULT_MODEL": "qwen2.5-coder:14b"
}
}
}
}
A Dockerfile is included. The server speaks MCP over stdio, so run it
interactively (-i) and point it at your Ollama instance:
docker build -t ollama-handoff .
docker run --rm -i -e OLLAMA_URL=http://host.docker.internal:11434 ollama-handoff
On native Linux (no Docker Desktop), use --network=host with
OLLAMA_URL=http://localhost:11434.
| Tool | What it does | When the agent should reach for it |
|---|---|---|
ask_local | One-shot prompt to the local model | Any handoff that doesn't need frontier reasoning |
chat_local | Multi-turn local chat | Handoffs needing more than one turn of context |
summarize_local | Structured summary (headline + bullets) | Long files, logs, transcripts, docs |
code_review_local | Quick first-pass review of a diff/code | Cheap pre-filter before a deep review |
draft_commit_message_local | Conventional commit message from a diff | Routine commits |
extract_local | Pull structured items from unstructured text | URLs, function names, error codes, TODOs |
list_models | List locally available Ollama models | Discovery / choosing a model |
server_info | Report the effective configuration | Debugging setup |
All configuration is via environment variables set in your MCP registration:
| Variable | Default | Description |
|---|---|---|
OLLAMA_URL | http://localhost:11434 | Base URL of the Ollama server |
OLLAMA_DEFAULT_MODEL | qwen2.5-coder:14b | Default model for handoffs |
OLLAMA_NUM_CTX | 32768 | Context window in tokens |
OLLAMA_KEEP_ALIVE | 30m | How long to keep the model resident in VRAM |
OLLAMA_TIMEOUT_S | 600 | Per-request timeout, seconds |
Once registered, you don't call the tools yourself — your agent does. A typical exchange:
You: Summarize the errors in
build.logand draft a commit for the staged fix.Agent: (calls
summarize_local(build.log, focus="errors and stack traces")anddraft_commit_message_local(git diff --staged)— both run on your GPU, nothing billed) → returns the summary + commit message.
git clone https://github.com/Michael-WhiteCapData/ollama-handoff
cd ollama-handoff
uv pip install -e ".[dev]"
ruff check .
pytest # tests use httpx.MockTransport — no running Ollama required
See CONTRIBUTING.md. Contributions welcome — especially new specialized handoff tools.
MIT © Michael Tierney
FAQs
An MCP server that offloads cheap work from your cloud LLM agent to a local Ollama model.
We found that ollama-handoff demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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