Build an AI Agent with Gemini & MCP

Short answer

You can build a multi-tool AI agent by exposing external APIs and actions as Model Context Protocol (MCP) tools, then letting Gemini discover and call them. This guide walks you through a simple, working architecture, tool definitions, and a tiny code example so you can ship a reliable agent fast.

Why use MCP with Gemini?

MCP is a standard way to let an LLM call external tools and APIs. That means Gemini stops guessing and starts doing real work: reading docs, calling services, or updating records. See a practical intro in the MCP deep dive: Model Context Protocol (MCP) with Gemini.

What you'll build

• A small orchestrator that accepts a user message.
• Two MCP tool servers: a Translation tool and a Gemini worker for reasoning.
• A simple routing rule so Gemini calls the right tool.

Time: about 1 hour. Prereqs: Node or Python, a Gemini API key, and a simple web host for your MCP tool endpoints.

5 clear steps to a working agent

1. Design the flow: User -> Orchestrator -> MCP tool servers (translation, data fetch, action).
2. Define tools with clear names, descriptions, and types so Gemini can pick them. Keep names short and inputs typed.
3. Implement MCP endpoints: Each tool serves a self-describing schema and an execute endpoint.
4. Wire Gemini so it can discover tools via MCP and call them using function-calling or the MCP protocol.
5. Test and tighten: Lower temperature, add structured responses, and add retries and timeouts.

How do you define a tool for MCP?

Make the tool self-describing. Give it a short name, a one-line description, and a strict input schema. Gemini uses that to decide when to call it.

{
  "name": "get_order_status",
  "description": "Return order status for an order id",
  "input": {
    "type": "object",
    "properties": {
      "order_id": {"type": "string"}
    },
    "required": ["order_id"]
  }
}

Example orchestration pattern

Use a routing orchestrator: a lightweight LLM or rule engine decides which tool(s) to run, then aggregates results. Routing avoids exposing all tools to every model call and keeps costs down. Read more about routing patterns and controlled generation here: Building with Gemini — Routing.

Minimal MCP server endpoints

• /tools — returns tool list and schemas.
• /execute — executes a named tool with typed input.

Keep responses small and strict. Gemini will act on the schema directly.

Code tip (pseudo)

// Orchestrator receives user message
// 1) Detect language. If not English, call "translate" tool.
// 2) Send English text to Gemini worker tool.
// 3) If Gemini returns an action, call the action tool.

Best practices for tool design

• Use clear tool names: e.g., search_docs, book_meeting.
• Strong typing: avoid free-form inputs when possible.
• Short descriptions: one sentence.
• Limit tool side effects: prefer preview steps before committing.
• Set model params: low temperature and single-choice outputs when you expect enums.

Error handling & observability

Build retries, timeouts, and a fallback answer. Log tool calls and responses so you can debug when Gemini picks the wrong tool. Tools should return structured errors like { code: 503, message: "timeout" }.

Testing strategy

• Unit test each tool endpoint with valid and invalid inputs.
• Run end-to-end tests that mock Gemini calls.
• Use a small set of user prompts to test routing and tool chaining.

Helpful links and references

• MCP overview and example: Build multilingual chatbots with Gemini, Gemma, and MCP.
• MCP deep dive with code: Medium: Model Context Protocol with Gemini.
• Routing and orchestration patterns: Building with Gemini — Routing.
• Gemini overview and multimodal docs: Gemini overview and developer resources at Google AI.
• Build-with-LLMs guide for workspace and MCP: Use Large Language Models to develop on Google.

Next steps — ship in under 1 hour

1. Pick one use case (e.g., order status).
2. Define two tools: translate + get_order_status.
3. Implement endpoints and a tiny orchestrator.
4. Connect Gemini via MCP discovery.
5. Run a few real prompts and iterate.

Want a longer walkthrough? The MCP articles and Gemini docs above give code samples and deeper context. This pattern scales: add more typed tools and a worker per major capability. Ship the smallest thing that proves the loop works, then expand.

Quick FAQ

Can Gemini call any API?

Yes, if you expose it via MCP with a clear schema. Keep auth and side effects safe.

Do I need special Gemini models?

Use a Gemini Pro or similar for hard reasoning and keep lightweight workers for simple tasks. See model options in the Gemini overview.

Close: Start small, keep tools typed, and tighten the model output. You’ll get a useful Gemini MCP agent that acts reliably and stays debuggable.