F
licenseNot graded
qualityD
maintenanceEnables AI-powered web searches using DuckDuckGo with automatic summarization via Google's Gemini AI. Supports both summary and detailed analysis modes for search results.
Gemini Search MCP
by kidapu
This server provides an AI-powered search and analysis agent using Gemini models with Google Search grounding to retrieve current, cited information from the web.
Web Search: Find up-to-date information on any topic via Google Search grounding, including content beyond the model's training cutoff
Error Troubleshooting: Research and diagnose technical issues or error messages
Topic Research: Deep-dive into subjects with AI-synthesized, web-sourced answers
Idea Discussion: Analyze and engage with complex concepts using AI-powered insights
Cited Responses: Receive answers backed by referenced web sources
Natural Language Queries: Ask questions in plain conversational language without special syntax
Model Flexibility: Choose from multiple Gemini variants (flash, pro, flash-lite) with different performance/price trade-offs, plus configurable thinking budget for reasoning depth
Provides access to Google's Gemini 2.5 AI models (Flash, Flash Lite, and Pro) with Google Search Grounding capabilities for web-enhanced AI responses.
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 Search MCPfind recent news about AI advancements in healthcare"
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.
@kidapu/gemini-search-mcp
Gemini 3.1 Flash-Lite + Google Search Grounding を使った MCP サーバー。
npm: https://www.npmjs.com/package/@kidapu/gemini-search-mcp
インストール
API キーは Google AI Studio で取得できます。
MCP サーバーとして追加（推奨）
# ユーザーレベル（全プロジェクトで使用可能）
claude mcp add gemini-search -s user -e GEMINI_SEARCH_MCP_API_KEY=your_api_key -- npx -y @kidapu/gemini-search-mcp
# プロジェクトレベル（このプロジェクトのみ）
claude mcp add gemini-search -s project -e GEMINI_SEARCH_MCP_API_KEY=your_api_key -- npx -y @kidapu/gemini-search-mcpプラグインとしてインストール
# 1. マーケットプレイスを追加
/plugin marketplace add kidapu/Gemini-Search-MCP
# 2. プラグインをインストール
/plugin install gemini-search@gemini-search-mcp注意: プラグイン方式では事前に環境変数の設定が必要です。
# ~/.zshrc に追記 export GEMINI_SEARCH_MCP_API_KEY="your_api_key" # 反映 source ~/.zshrc
Related MCP server: Deno Gemini Grounding MCP Server
環境変数
変数名 | デフォルト | 説明 |
| - | Google AI API キー（必須） |
|
| モデル: |
|
| Thinking: -1=auto, 0=off, 1-24576 |
|
| Web検索: true/false |
モデル
エイリアス | フルネーム | 特徴 |
|
| 最速・最安・高性能（デフォルト） |
|
| ↑ と同じ |
|
| 最高性能 |
|
| 旧デフォルト |
|
| 旧最安 |
フルネームでも指定可能（例: GEMINI_MODEL=gemini-3-flash-preview）。
料金
モデル | Input (1M tokens) | Output (1M tokens) |
| $0.25 | $1.50 |
| $0.50 | $3.00 |
| $0.30 | $2.50 |
| $0.10 | $0.40 |
| $1.25 | $10.00 |
最新の料金は Gemini API Pricing を参照してください。
Available Tools
1 toolgemini-searchB
An AI agent powered by Gemini 2.5 Flash with Google Search grounding. Useful for finding the latest information, troubleshooting errors, researching topics, and discussing ideas. Returns responses with cited sources from the web.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language query to search and analyze. Ask questions, search for information, or request analysis of complex topics. |
TDQS
B3.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's an AI agent with search grounding, returns responses with cited sources, and handles natural language queries. However, it lacks details on rate limits, authentication needs, or specific limitations like response format or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey the tool's core functionality and key features. Every sentence adds value, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (AI agent with search grounding), no annotations, no output schema, and 100% schema coverage, the description is moderately complete. It covers the purpose and basic behavior but lacks details on output format, error handling, or advanced usage, which could be important for an AI-driven tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents the single parameter 'query' with a clear description. The description adds minimal value beyond this, mentioning 'natural language query' and use cases, but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: an AI agent powered by Gemini 2.5 Flash with Google Search grounding for finding latest information, troubleshooting, researching, and discussing ideas. It specifies the verb ('search and analyze') and resource ('information from the web'), but lacks sibling differentiation since there are no sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidelines by listing scenarios ('finding latest information, troubleshooting errors, researching topics, and discussing ideas'), but does not explicitly state when to use this tool versus alternatives or include exclusions. With no sibling tools, this is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
B3.4/5.0
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a clearly defined purpose focused on AI-powered search with web grounding.
Naming Consistency5/5
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'gemini-search' follows a clear and descriptive pattern.
Tool Count2/5
A single tool is too few for a server named 'Gemini Search MCP', which suggests a broader search functionality scope. While the tool is powerful, the set feels thin and limited, lacking complementary operations like filtering, saving results, or managing search history.
Completeness2/5
The tool surface is severely incomplete for a search domain. It provides a general search function but lacks essential operations such as refining searches, handling pagination, accessing cached results, or integrating with other data sources, which are typical in search APIs.
Maintenance
ActivityInactive
ResponsivenessSyncing
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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