add-ollama-tool
Add Ollama MCP server so the container agent can call local models for cheaper/faster tasks like summarization, translation, or general queries.
Best use case
add-ollama-tool is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Add Ollama MCP server so the container agent can call local models for cheaper/faster tasks like summarization, translation, or general queries.
Teams using add-ollama-tool should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/add-ollama-tool/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How add-ollama-tool Compares
| Feature / Agent | add-ollama-tool | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Add Ollama MCP server so the container agent can call local models for cheaper/faster tasks like summarization, translation, or general queries.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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. Tools added: - `ollama_list_models` — lists installed Ollama models - `ollama_generate` — sends a prompt to a specified model and returns the response ## Phase 1: Pre-flight ### Check if already applied Check if `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: ```bash 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: > > ```bash > 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 ```bash git remote -v ``` If `upstream` is missing, add it: ```bash git remote add upstream https://github.com/sbusso/claudeclaw.git ``` ### Merge the skill branch ```bash git fetch upstream skill/ollama-tool git merge upstream/skill/ollama-tool ``` This merges in: - `agent/runner/src/ollama-mcp-stdio.ts` (Ollama MCP server) - `scripts/ollama-watch.sh` (macOS notification watcher) - Ollama MCP config in `agent/runner/src/index.ts` (allowedTools + mcpServers) - `[OLLAMA]` log surfacing in `src/orchestrator/container-runner.ts` - `OLLAMA_HOST` in `.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: ```bash for dir in data/sessions/*/agent-runner-src; do cp agent/runner/src/ollama-mcp-stdio.ts "$dir/" cp agent/runner/src/index.ts "$dir/" done ``` ### Validate code changes ```bash npm run build ./src/runtimes/docker/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`: ```bash OLLAMA_HOST=http://your-ollama-host:11434 ``` > **Service name:** Derived from the directory name: `com.claudeclaw.<dirname>` (macOS) / `claudeclaw-<dirname>` (Linux). For example, if cwd is `my-assistant`, the service is `com.claudeclaw.my-assistant`. Determine the correct service name before running service commands below. ### Restart the service ```bash launchctl kickstart -k gui/$(id -u)/com.claudeclaw # macOS # Linux: systemctl --user restart claudeclaw ``` ## Phase 4: Verify ### Test via WhatsApp Tell the user: > Send a message like: "use ollama to tell me the capital of France" > > The agent should use `ollama_list_models` to find available models, then `ollama_generate` to get a response. ### Monitor activity (optional) Run the watcher script for macOS notifications when Ollama is used: ```bash ./scripts/ollama-watch.sh ``` ### Check logs if needed ```bash tail -f logs/claudeclaw.log | grep -i ollama ``` Look for: - `Agent output: ... Ollama ...` — agent used Ollama successfully - `[OLLAMA] >>> Generating` — generation started (if log surfacing works) - `[OLLAMA] <<< Done` — generation completed ## 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: 1. The MCP server wasn't registered — check `agent/runner/src/index.ts` has the `ollama` entry in `mcpServers` 2. The per-group source wasn't updated — re-copy files (see Phase 2) 3. The container wasn't rebuilt — run `./src/runtimes/docker/build.sh` ### "Failed to connect to Ollama" 1. Verify Ollama is running: `ollama list` 2. Check Docker can reach the host: `docker run --rm curlimages/curl curl -s http://host.docker.internal:11434/api/tags` 3. If using a custom host, check `OLLAMA_HOST` in `.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: ..."
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