MCP Plugin Skills

Skills for adding MCP (Model Context Protocol) plugins that provide external tools to agents inside containers.

What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI agents call external tools through a unified interface. In nagi, MCP plugins run inside Docker containers alongside the agent. The host process registers each plugin with the orchestrator, which spawns the MCP server and exposes its tools to the agent automatically.

When you add an MCP plugin, three things happen:

1. The plugin package is included in the Docker image at build time.
2. deploy/{ASSISTANT_NAME}/host/entry.ts registers the plugin with orchestrator.registerMcpPlugin().
3. At runtime, the agent sees the plugin's tools (prefixed mcp__<plugin>__) and can call them like any other tool.

---

/add-mcp-ollama --- Add Ollama

Add the Ollama MCP plugin so container agents can access local LLM models for cheaper or faster tasks.

Triggers: add ollama, setup ollama, enable ollama

Prerequisites

• Ollama installed and running on the host. macOS: brew install ollama; Linux: curl -fsSL https://ollama.com/install.sh | sh; or download from https://ollama.com.
• At least one model pulled. Run ollama pull <model> before using the plugin.
• Network reachability. The container reaches the host via host.docker.internal:11434. On Linux, nagi automatically adds --add-host=host.docker.internal:host-gateway.

Supported Models (examples)

Model	Size	Notes
llama3.2	~2 GB	Fast, good general-purpose
mistral	~4 GB	Strong reasoning quality
gemma2	~5 GB	Google's open model

Any model available in your local Ollama library can be used. Run ollama list to see what is installed.

Available Tools

Once configured, container agents have access to:

Tool	Description
ollama_list_models	List installed local models with sizes
ollama_generate	Send a prompt to a local model and get a response

Configuration

The skill performs the following steps:

1. Pre-flight --- Checks that Ollama is installed, running, and has at least one model.
2. Register plugin --- Adds the Ollama MCP plugin to deploy/{ASSISTANT_NAME}/host/entry.ts:

   orchestrator.registerMcpPlugin("ollama", {
     entryPoint: "/app/mcp-plugins/ollama/dist/index.js",
   });

   No API token is needed because Ollama runs locally.
3. Custom host (optional) --- If Ollama runs on a different machine or port, pass the address via an environment variable:

   orchestrator.registerMcpPlugin("ollama", {
     entryPoint: "/app/mcp-plugins/ollama/dist/index.js",
     env: { OLLAMA_HOST: "http://192.168.1.100:11434" },
   });

4. Rebuild & verify --- Rebuild the Docker image (./container/claude-code/build.sh or ./container/open-code/build.sh), restart nagi, and test via Slack/Discord.

Troubleshooting

Symptom	Fix
"Failed to connect to Ollama"	Ensure ollama serve is running. Verify with curl http://localhost:11434/api/tags.
Agent does not see Ollama tools	Confirm registerMcpPlugin("ollama", ...) is in entry.ts, rebuild the image, and restart.
No models available	Pull a model: ollama pull llama3.2

---

/add-mcp-vercel --- Add Vercel

Add the Vercel MCP plugin so container agents can deploy websites, manage projects, and inspect deployments.

Triggers: add vercel, setup vercel, enable vercel

Prerequisites

• A Vercel account at https://vercel.com.
• An API token. Create one at https://vercel.com/account/tokens with at least deploy-level scope.

Available Tools

Once configured, container agents have access to:

Tool	Description
vercel_list_projects	List Vercel projects
vercel_create_project	Create a new project
vercel_deploy	Deploy files and receive a live URL
vercel_list_deployments	List recent deployments
vercel_get_deployment	Get details of a specific deployment
vercel_delete_project	Delete a project

Configuration

The skill performs the following steps:

1. Pre-flight --- Checks whether VERCEL_API_TOKEN already exists in .env.
2. Get API token --- Guides you through creating a token on the Vercel dashboard if you do not have one, then saves it to .env.
3. Register plugin --- Adds the Vercel MCP plugin to deploy/{ASSISTANT_NAME}/host/entry.ts:

   const vercelEnv = readEnvFile(["VERCEL_API_TOKEN"]);
   if (vercelEnv.VERCEL_API_TOKEN) {
     orchestrator.registerMcpPlugin("vercel", {
       entryPoint: "/app/mcp-plugins/vercel/dist/index.js",
       env: { VERCEL_API_TOKEN: vercelEnv.VERCEL_API_TOKEN },
     });
   }

4. Rebuild & verify --- Rebuild the Docker image, restart nagi, and test by asking the agent to list projects or deploy a page.

Troubleshooting

Symptom	Fix
"VERCEL_API_TOKEN is not set"	Add the token to .env at the project root AND ensure entry.ts passes it via registerMcpPlugin.
Agent does not see Vercel tools	Confirm registerMcpPlugin("vercel", ...) is in entry.ts, rebuild the image, and restart.
Deploy fails	Verify the token: curl -s -H "Authorization: Bearer $VERCEL_API_TOKEN" https://api.vercel.com/v9/projects. Check that the token scope includes deploy permissions.