Adds four new MCP tools to the existing ollama integration, consolidating model management (from #1331) into the single add-ollama-tool skill as requested by @gavrielc: - ollama_pull_model — pull a model from the Ollama registry - ollama_delete_model — delete a local model to free disk space - ollama_show_model — inspect modelfile, parameters, and architecture - ollama_list_running — list models loaded in memory with VRAM/processor info All four tools follow the existing patterns in this file: OLLAMA_HOST env var, ollamaFetch() with host.docker.internal fallback, log() and writeStatus() helpers. No changes to index.ts or container-runner.ts needed — OLLAMA_HOST is already forwarded via sdkEnv. Also updates SKILL.md description, tool list, verify steps, and adds a troubleshooting entry for large-model pull timeouts. Closes #1331. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
5.0 KiB
name, description
| name | description |
|---|---|
| add-ollama-tool | Add Ollama MCP server so the container agent can call local models and manage the Ollama model library. |
Add Ollama Integration
This skill adds a stdio-based MCP server that exposes local Ollama models as tools for the container agent. Claude remains the orchestrator but can offload work to local models, and can also manage the model library directly.
Tools added:
ollama_list_models— list installed models with name, size, family, and last modified dateollama_generate— send a prompt to a specified model and return the responseollama_pull_model— pull (download) a model from the Ollama registry by nameollama_delete_model— delete a locally installed model to free disk spaceollama_show_model— show model details: modelfile, parameters, template, and architecture infoollama_list_running— list models currently loaded in memory with memory usage and processor type
Phase 1: Pre-flight
Check if already applied
Check if container/agent-runner/src/ollama-mcp-stdio.ts exists. If it does, skip to Phase 3 (Configure).
Check prerequisites
Verify Ollama is installed and running on the host:
ollama list
If Ollama is not installed, direct the user to https://ollama.com/download.
If no models are installed, suggest pulling one:
You need at least one model. I recommend:
ollama pull gemma3:1b # Small, fast (1GB) ollama pull llama3.2 # Good general purpose (2GB) ollama pull qwen3-coder:30b # Best for code tasks (18GB)
Phase 2: Apply Code Changes
Ensure upstream remote
git remote -v
If upstream is missing, add it:
git remote add upstream https://github.com/qwibitai/nanoclaw.git
Merge the skill branch
git fetch upstream skill/ollama-tool
git merge upstream/skill/ollama-tool
This merges in:
container/agent-runner/src/ollama-mcp-stdio.ts(Ollama MCP server)scripts/ollama-watch.sh(macOS notification watcher)- Ollama MCP config in
container/agent-runner/src/index.ts(allowedTools + mcpServers) [OLLAMA]log surfacing insrc/container-runner.tsOLLAMA_HOSTin.env.example
If the merge reports conflicts, resolve them by reading the conflicted files and understanding the intent of both sides.
Copy to per-group agent-runner
Existing groups have a cached copy of the agent-runner source. Copy the new files:
for dir in data/sessions/*/agent-runner-src; do
cp container/agent-runner/src/ollama-mcp-stdio.ts "$dir/"
cp container/agent-runner/src/index.ts "$dir/"
done
Validate code changes
npm run build
./container/build.sh
Build must be clean before proceeding.
Phase 3: Configure
Set Ollama host (optional)
By default, the MCP server connects to http://host.docker.internal:11434 (Docker Desktop) with a fallback to localhost. To use a custom Ollama host, add to .env:
OLLAMA_HOST=http://your-ollama-host:11434
Restart the service
launchctl kickstart -k gui/$(id -u)/com.nanoclaw # macOS
# Linux: systemctl --user restart nanoclaw
Phase 4: Verify
Test inference
Tell the user:
Send a message like: "use ollama to tell me the capital of France"
The agent should use
ollama_list_modelsto find available models, thenollama_generateto get a response.
Test model management
Send a message like: "pull the gemma3:1b model" or "which ollama models are currently loaded in memory?"
The agent should call
ollama_pull_modelorollama_list_runningrespectively.
Monitor activity (optional)
Run the watcher script for macOS notifications when Ollama is used:
./scripts/ollama-watch.sh
Check logs if needed
tail -f logs/nanoclaw.log | grep -i ollama
Look for:
[OLLAMA] >>> Generating— generation started[OLLAMA] <<< Done— generation completed[OLLAMA] Pulling model:— pull in progress[OLLAMA] Deleted:— model removed
Troubleshooting
Agent says "Ollama is not installed"
The agent is trying to run ollama CLI inside the container instead of using the MCP tools. This means:
- The MCP server wasn't registered — check
container/agent-runner/src/index.tshas theollamaentry inmcpServers - The per-group source wasn't updated — re-copy files (see Phase 2)
- The container wasn't rebuilt — run
./container/build.sh
"Failed to connect to Ollama"
- Verify Ollama is running:
ollama list - Check Docker can reach the host:
docker run --rm curlimages/curl curl -s http://host.docker.internal:11434/api/tags - If using a custom host, check
OLLAMA_HOSTin.env
Agent doesn't use Ollama tools
The agent may not know about the tools. Try being explicit: "use the ollama_generate tool with gemma3:1b to answer: ..."
ollama_pull_model times out on large models
Large models (7B+) can take several minutes. The tool uses stream: false so it blocks until complete — this is intentional. For very large pulls, use the host CLI directly: ollama pull <model>