Provides access to Google's Gemini Pro API for text generation and image analysis using Gemini Pro Vision capabilities
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Gemini Pro MCP Server generate a summary of quantum computing basics"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for integrating Gemini Pro API with Claude Desktop.
Install dependencies:
npm installGet your Gemini API key:
Go to Google AI Studio
Create a new API key
Copy the key
Set up environment variable:
export GEMINI_API_KEY="your-api-key-here"Build the project:
npm run buildConfigure Claude Desktop:
Edit your Claude Desktop config file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
Add this configuration:
{
"mcpServers": {
"gemini-pro": {
"command": "node",
"args": ["/Users/luis_ticas/Documents/gemini-mcp-server/dist/index.js"],
"env": {
"GEMINI_API_KEY": "your-gemini-api-key-here"
}
}
}
}Restart Claude Desktop
Related MCP server: MCP Gemini Server
generate_text: Generate text using Gemini Pro
analyze_image: Analyze images using Gemini Pro Vision
Once connected, you can use commands like:
"Generate text using Gemini about [topic]"
"Analyze this image using Gemini"
Analyze an image using Gemini 2.5 Pro Vision
| Name | Required | Description | Default |
|---|---|---|---|
| imageData | Yes | Base64 encoded image data | |
| prompt | No | Text prompt to describe what to analyze in the image | Describe this image |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the specific model ('Gemini 2.5 Pro Vision'), it doesn't describe what the analysis entails, what format the results take, whether there are rate limits, authentication requirements, or any constraints on image size/format. For an AI vision tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a tool with two parameters and clearly states the core functionality. Every word earns its place without redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an AI vision analysis tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of analysis is performed, what the output looks like, or any limitations/constraints. The agent knows what the tool is called and what model it uses, but not what to expect from using it or what constitutes appropriate usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any additional meaning about the parameters beyond what's in the schema. It doesn't explain what constitutes appropriate 'imageData' or provide guidance on crafting effective 'prompt' values. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('analyze') and resource ('an image'), and specifies the method ('using Gemini 2.5 Pro Vision'). It distinguishes from the sibling tool 'generate_text' by focusing on image analysis rather than text generation. However, it doesn't explicitly contrast with the sibling tool in the description text itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate, what types of analysis it performs, or how it differs from the sibling 'generate_text' tool. The agent must infer usage from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate text using Gemini 2.5 Pro model
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The text prompt to send to Gemini | |
| maxTokens | No | Maximum number of tokens to generate (optional) | |
| temperature | No | Temperature for text generation (0.0 to 2.0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the specific model ('Gemini 2.5 Pro') which is useful context, but fails to disclose critical behavioral traits like rate limits, authentication requirements, cost implications, response format, or error handling. For a text generation tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized for a straightforward tool and front-loads the essential information. Every word earns its place in this concise statement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a text generation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how to interpret results, error conditions, or practical limitations. While the schema covers parameters well, the overall context for using this tool effectively is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation through the schema alone, with no value added by the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate text') and specifies the resource ('using Gemini 2.5 Pro model'), making the purpose immediately understandable. It distinguishes from the sibling 'analyze_image' by focusing on text generation rather than image analysis. However, it doesn't specify what kind of text generation (e.g., creative, factual, code) which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of use cases, prerequisites, or comparison with other text generation methods. The agent must infer usage purely from the tool name and parameters without any contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have completely distinct purposes: one analyzes images and the other generates text. There is no overlap in functionality, making it impossible to confuse them.
Both tools follow a consistent verb_noun pattern (analyze_image, generate_text) with clear, descriptive names that indicate their specific actions and targets.
With only two tools, the server feels under-scoped for a Gemini Pro MCP Server, which might be expected to offer more capabilities like editing, summarizing, or multimodal interactions beyond basic image analysis and text generation.
The toolset is severely incomplete for a Gemini Pro server, lacking essential operations such as editing text, summarizing content, handling multimodal inputs beyond images, or providing model configuration options, which limits agent workflows.
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