Add mcp ollama
Add Ollama MCP plugin for local LLM access from agent containers. Triggers on "add ollama", "setup ollama", "enable ollama".From its SKILL.md
npx -y skills add yukihirop/nagi --skill add-mcp-ollamaAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Add Ollama MCP Plugin
Step 0: Language selection
Before proceeding with any other steps in this skill, ask the user which language to continue in using AskUserQuestion. Keep this initial prompt in English because the preferred language is not yet known.
- Question:
Which language should I continue in? - Options:
English,日本語 (Japanese)
Use the selected language for all subsequent user-facing messages and for every further AskUserQuestion prompt in this skill. Do not translate code, file paths, shell commands, or file contents.
This skill configures the Ollama MCP plugin so container agents can use local LLM models (llama3.2, mistral, gemma2, etc.) for cheaper/faster tasks.
UX Note: Use AskUserQuestion for all user-facing questions.
Phase 1: Pre-flight
Check Ollama is installed
which ollama 2>/dev/null && ollama --version || echo "NOT_FOUND"
If not installed, AskUserQuestion: Install Ollama?
- macOS:
brew install ollama - Linux:
curl -fsSL https://ollama.com/install.sh | sh - Or download from https://ollama.com
Check Ollama is running
curl -s http://localhost:11434/api/tags | head -1 || echo "NOT_RUNNING"
If not running: ollama serve & or start the Ollama app.
Check models are installed
ollama list
If no models, suggest pulling one:
ollama pull llama3.2
Phase 2: Configure entry.ts
Verify deploy/{ASSISTANT_NAME}/host/entry.ts contains the Ollama MCP plugin registration. If not, add this block after the orchestrator creation:
orchestrator.registerMcpPlugin("ollama", {
entryPoint: "/app/mcp-plugins/ollama/dist/index.js",
});
No API token needed — Ollama runs locally. The container reaches the host via host.docker.internal:11434.
If deploy/{ASSISTANT_NAME}/host/entry.ts is outdated, compare with deploy/templates/host/entry.template.ts and update accordingly.
Custom Ollama host (optional)
If Ollama runs on a different host/port, pass it as an environment variable:
orchestrator.registerMcpPlugin("ollama", {
entryPoint: "/app/mcp-plugins/ollama/dist/index.js",
env: { OLLAMA_HOST: "http://192.168.1.100:11434" },
});
Phase 3: Rebuild & Verify
Rebuild Docker image (if not already built with Ollama plugin)
Check which agent type is configured (CONTAINER_IMAGE in .env), then rebuild the matching image:
Claude Code:
./container/claude-code/build.sh
Open Code:
./container/open-code/build.sh
Restart nagi
pnpm dev
Test
Tell user:
Send a message in your Slack channel:
- "What models does Ollama have?"
- "Use Ollama with llama3.2 to summarize: The quick brown fox jumps over the lazy dog."
The agent should use
mcp__ollama__ollama_list_modelsormcp__ollama__ollama_generate.
Available Tools
Once configured, container agents have access to:
ollama_list_models— List installed local models with sizesollama_generate— Send a prompt to a local model and get a response
Troubleshooting
"Failed to connect to Ollama"
- Ollama must be running on the host:
ollama serveor start the Ollama app - Verify:
curl http://localhost:11434/api/tags - Docker must be able to reach the host —
host.docker.internalis used automatically on macOS/Windows. On Linux, ensure--add-host=host.docker.internal:host-gatewayis set (nagi handles this automatically).
Agent doesn't see Ollama tools
- Check
deploy/{ASSISTANT_NAME}/host/entry.tshasregisterMcpPlugin("ollama", ...) - Check Docker image was rebuilt:
./container/claude-code/build.sh(or./container/open-code/build.shfor Open Code) - Restart nagi
No models available
Pull a model on the host:
ollama pull llama3.2 # 2GB, fast
ollama pull mistral # 4GB, good quality
ollama pull gemma2 # 5GB, Google's model
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most mcp tooling skills give in ~1.0k tokens
Counted across 638 of the 750 authors here whose files we hold, read 2026-08-07
- Create ten complex or independent read-only evaluation questionsin 69 of 638, across 15 files
- Test servers using MCP Inspectorin 61 of 638, across 19 files
- Provide actionable error messages with specific next stepsin 54 of 638, across 12 files
- Prioritize comprehensive API coverage over specific workflows or workflow toolsin 54 of 638, across 12 files
- Use TypeScript and Streamable HTTP for remote servers or clientsin 54 of 638, across 8 files
- Define structured output schemas where possiblein 50 of 638, across 8 files
- Use Zod or Pydantic for input schemasin 47 of 638, across 5 files
- Fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
- Load framework documentation using WebFetchin 45 of 638, across 3 files
- Verify each evaluation answer independentlyin 45 of 638, across 3 files
- Implement API client with authentication and paginationin 45 of 638, across 3 files
- Define input schemas with validationin 27 of 638, across 9 files
Said here and by no other author read
- Ask user which language to use
- Check if Ollama is installed
- Start Ollama if not running
- Check if any models are installed
- Pull a model if none exist
- Verify Ollama MCP plugin registration exists
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.