> ## Documentation Index
> Fetch the complete documentation index at: https://www.klavis.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini

> Learn how to build AI agents that integrate Google's Gemini with Strata MCP servers to build AI agents that can interact with Gmail and Slack.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/klavis-ai/klavis/blob/main/examples/google-genai/Use_Klavis_with_Gemini.ipynb)

# Gemini + Klavis AI Integration

This tutorial demonstrates how to use Google's Gemini with function calling with Klavis MCP (Model Context Protocol) servers.

## Prerequisites

Before we begin, you'll need:

<CardGroup cols={2}>
  <Card title="Google AI API Key" icon="key" href="https://ai.google.dev/">
    Get your API key from Google AI Studio
  </Card>

  <Card title="Klavis AI API Key" icon="key" href="https://klavis.ai/">
    Get your API key from Klavis AI
  </Card>
</CardGroup>

## Installation

First, install the required packages:

<CodeGroup>
  ```bash Python theme={null}
  pip install google-genai klavis
  ```

  ```bash TypeScript theme={null}
  npm install @google/genai klavis
  ```
</CodeGroup>

## Full Code Examples

For complete working examples, check out the source code:

<CardGroup cols={2}>
  <Card title="Python Example" icon="python" href="https://github.com/Klavis-AI/klavis/blob/main/examples/google-genai/python/main.py" />

  <Card title="TypeScript Example" icon="code" href="https://github.com/Klavis-AI/klavis/blob/main/examples/google-genai/typescript/main.ts" />
</CardGroup>

## Setup Environment Variables

<CodeGroup>
  ```python Python theme={null}
  import os

  # Set environment variables
  os.environ["GEMINI_API_KEY"] = "YOUR_GEMINI_API_KEY"  # Replace with your actual Gemini API key
  os.environ["KLAVIS_API_KEY"] = "YOUR_KLAVIS_API_KEY"  # Replace with your actual Klavis API key
  ```

  ```typescript TypeScript theme={null}
  import { GoogleGenAI } from '@google/genai';
  import { KlavisClient, Klavis } from 'klavis';

  // Set environment variables
  process.env.GEMINI_API_KEY = "YOUR_GEMINI_API_KEY";  // Replace with your actual Gemini API key
  process.env.KLAVIS_API_KEY = "YOUR_KLAVIS_API_KEY";  // Replace with your actual Klavis API key
  ```
</CodeGroup>

### Step 1 - Create Strata MCP Server with Gmail and Slack

<CodeGroup>
  ```python Python theme={null}
  from klavis import Klavis
  from klavis.types import McpServerName, ToolFormat
  import webbrowser

  klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

  response = klavis_client.mcp_server.create_strata_server(
      servers=[McpServerName.GMAIL, McpServerName.SLACK], 
      user_id="1234"
  )

  # Handle OAuth authorization for each services
  if response.oauth_urls:
      for server_name, oauth_url in response.oauth_urls.items():
          webbrowser.open(oauth_url)
          print(f"Or please open this URL to complete {server_name} OAuth authorization: {oauth_url}")
  ```

  ```typescript TypeScript theme={null}
  const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });

  const response = await klavisClient.mcpServer.createStrataServer({
      servers: [Klavis.McpServerName.Gmail, Klavis.McpServerName.Slack],
      userId: "1234"
  });

  // Handle OAuth authorization for each services
  if (response.oauthUrls) {
      for (const [serverName, oauthUrl] of Object.entries(response.oauthUrls)) {
          window.open(oauthUrl);
          // Wait for user to complete OAuth
          await new Promise(resolve => {
              const input = prompt(`Press OK after completing ${serverName} OAuth authorization...`);
              resolve(input);
          });
      }
  }

  ```
</CodeGroup>

<Note>
  **OAuth Authorization Required**: The code above will open browser windows for each service. Click through the OAuth flow to authorize access to your accounts.
</Note>

### Step 2 - Create method to use MCP Server with Claude

This method handles multiple rounds of tool calls until a final response is ready, allowing the AI to chain tool executions for complex tasks.

<CodeGroup>
  ```python Python theme={null}
  import json
  from google import genai
  from google.genai import types

  def gemini_with_mcp_server(mcp_server_url: str, user_query: str):
      gemini_client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

      contents = []
      contents.append(types.Content(role="user", parts=[types.Part(text=user_query)]))

      mcp_server_tools = klavis_client.mcp_server.list_tools(
          server_url=mcp_server_url,
          format=ToolFormat.GEMINI
      )

      max_iterations = 10
      iteration = 0

      while iteration < max_iterations:
          iteration += 1

          response = gemini_client.models.generate_content(
              model='gemini-2.5-flash',
              contents=contents,
              config=types.GenerateContentConfig(tools=mcp_server_tools.tools)
          )

          if response.candidates and response.candidates[0].content.parts:
              contents.append(response.candidates[0].content)

              # Check if there are function calls to execute
              has_function_calls = False
              for part in response.candidates[0].content.parts:
                  if hasattr(part, 'function_call') and part.function_call:
                      has_function_calls = True
                      function_name = part.function_call.name
                      function_args = dict(part.function_call.args)

                      print(f"Calling: {function_name}, with args: {function_args}")

                      result = klavis_client.mcp_server.call_tools(
                          server_url=mcp_server_url,
                          tool_name=function_name,
                          tool_args=function_args
                      )

                      function_response_part = types.Part.from_function_response(
                          name=function_name,
                          response={'result': result.result}
                      )
                      function_response_content = types.Content(
                          role='tool',
                          parts=[function_response_part]
                      )
                      contents.append(function_response_content)

              if has_function_calls:
                  continue
              else:
                  return response.text
          else:
              return "No response generated."

      return "Max iterations reached without final response"
  ```

  ```typescript TypeScript theme={null}
  async function geminiWithMcpServer(mcpServerUrl: string, userQuery: string) {
      const geminiClient = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

      const contents = [];
      contents.push({ role: "user", parts: [{ text: userQuery }] });

      const mcpServerTools = await klavisClient.mcpServer.listTools({
          serverUrl: mcpServerUrl,
          format: Klavis.ToolFormat.Gemini
      });

      const maxIterations = 10;
      let iteration = 0;

      while (iteration < maxIterations) {
          iteration++;

          const response = await geminiClient.models.generateContent({
              model: 'gemini-2.5-flash',
              contents: contents,
              config: { tools: mcpServerTools.tools }
          });

          if (response.candidates && response.candidates[0].content.parts) {
              contents.push(response.candidates[0].content);

              // Check if there are function calls to execute
              let hasFunctionCalls = false;
              for (const part of response.candidates[0].content.parts) {
                  if (part.functionCall) {
                      hasFunctionCalls = true;
                      const functionName = part.functionCall.name;
                      const functionArgs = part.functionCall.args;

                      console.log(`🔧 Calling: ${functionName}, with args:`, functionArgs);

                      const result = await klavisClient.mcpServer.callTools({
                          serverUrl: mcpServerUrl,
                          toolName: functionName,
                          toolArgs: functionArgs
                      });

                      const functionResponsePart = {
                          functionResponse: {
                              name: functionName,
                              response: { result: result.result }
                          }
                      };
                      const functionResponseContent = {
                          role: 'tool',
                          parts: [functionResponsePart]
                      };
                      contents.push(functionResponseContent);
                  }
              }

              if (hasFunctionCalls) {
                  continue;
              } else {
                  return response.text;
              }
          } else {
              return "No response generated.";
          }
      }

      return "Max iterations reached without final response";
  }
  ```
</CodeGroup>

### Step 3 - Run!

<CodeGroup>
  ```python Python theme={null}
  result = gemini_with_mcp_server(
      mcp_server_url=response.strata_server_url,
      user_query="Check my latest 5 gmails and summarize them in a Slack message to #engineering"
  )

  print(f"\n🤖 Final Response: {result}")
  ```

  ```typescript TypeScript theme={null}
  const result = await geminiWithMcpServer(
      response.strataServerUrl,
      "Check my latest 5 gmails and summarize them in a Slack message to #engineering"
  );

  console.log(`\n🤖 Final Response: ${result}`);
  ```
</CodeGroup>

<Check>
  Perfect! You've integrated Gemini with Klavis MCP servers.
</Check>

## Next Steps

<CardGroup cols={2}>
  <Card title="Explore More MCP Servers" icon="server">
    Try other available servers like Slack, Notion, GitHub, etc.
  </Card>

  <Card title="Multimodal Workflows" icon="image">
    Build workflows that combine text, images, and other media
  </Card>

  <Card title="Production Deployment" icon="rocket">
    Scale these patterns for production applications
  </Card>

  <Card title="Custom Integrations" icon="code">
    Build custom MCP servers for your specific needs
  </Card>
</CardGroup>

## Useful Resources

* [Google AI Documentation](https://ai.google.dev/)
* [Gemini API Reference](https://ai.google.dev/api)
* [Klavis AI Documentation](https://www.klavis.ai/docs/)
* [MCP Protocol Specification](https://modelcontextprotocol.io/)
* [Klavis MCP Servers](/mcp-server)

**Happy building!** 🚀