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GemForge-Gemini-Tools-MCP

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GemForge（ジェミニツールズ）

鍛冶屋バッジ MCP.so

GemForge-Gemini-Tools-MCP ：お気に入りのMCPエージェント向けのエンタープライズグレードのGemini統合。コードベース分析、ライブ検索、テキスト/PDF/画像処理などにより、Claude、Roo Code、Windsurfを強化します。

クイックナビゲーション

Related MCP server: Vibe Check MCP

GemForgeを選ぶ理由

GemForge は、Google の Gemini AI と MCP エコシステムをつなぐ重要な架け橋です。 宝石の霧

  • リアルタイムウェブアクセス: gemini_searchで最新ニュース、市場動向、現在のデータを取得します

  • 高度な推論： gemini_reasonを使って段階的に思考し、複雑な論理問題を処理する

  • コードマスタリー: gemini_codeを使用してリポジトリ全体を分析、ソリューションを生成、コードをデバッグする

  • マルチファイル処理: gemini_fileopsで PDF、画像など 60 種類以上のファイル形式を処理

  • インテリジェントなモデル選択: 各タスクに最適な Gemini モデルを自動的にルーティングします

  • エンタープライズ対応: 堅牢なエラー処理、レート制限管理、API フォールバック メカニズム

クイックスタート

ワンラインインストール

npx @gemforge/mcp-server@latest init

手動設定

  1. 構成ファイル ( claude_desktop_config.json ) を作成します。

{
  "mcpServers": {
    "GemForge": {
      "command": "node",
      "args": ["./dist/index.js"],
      "env": {
        "GEMINI_API_KEY": "your_api_key_here"
      }
    }
  }
}

  1. インストールして実行:

npm install gemforge-mcp
npm start

30秒のセットアップデモを見る→

ヘビーデューティーな信頼性

GemForge は実稼働環境向けに構築されています。

  • 60種類以上のファイル形式をサポート：コードからドキュメント、画像まであらゆるものを処理

  • 自動モデルフォールバック: レート制限やサービス中断時でも機能を継続します

  • エンタープライズグレードのエラーログ:トラブルシューティングのための詳細な診断

  • API のレジリエンス: 指数バックオフ、再試行ロジック、シームレスなモデル切り替え

  • 完全なリポジトリサポート: 設定可能な包含/除外パターンを使用してコードベース全体を分析します

  • XMLコンテンツ処理：構造化データの特殊な処理

主要ツール

道具

説明

主な機能

gemini_search

ウェブ接続情報検索

リアルタイムデータアクセス

gemini_reason

ステップバイステップのロジックによる複雑な問題解決

透明な推論プロセス

gemini_code

コードの深い理解と生成

完全なリポジトリ分析

gemini_fileops

60以上のフォーマットにわたるマルチファイル処理

ドキュメントの比較と変換

{
  "toolName": "gemini_search",
  "toolParams": {
    "query": "Latest advancements in quantum computing",
    "enable_thinking": true
  }
}
{
  "toolName": "gemini_code",
  "toolParams": {
    "question": "Identify improvements and new features",
    "directory_path": "path/to/project",
    "repomix_options": "--include \"**/*.js\" --no-gitignore"
  }
}
{
  "toolName": "gemini_fileops",
  "toolParams": {
    "file_path": ["contract_v1.pdf", "contract_v2.pdf"],
    "operation": "analyze",
    "instruction": "Compare these contract versions and extract all significant changes."
  }
}

構成

GemForge は柔軟な構成オプションを提供します:

GEMINI_API_KEY=your_api_key_here       # Required: Gemini API key
GEMINI_PAID_TIER=true                  # Optional: Set to true if using paid tier (better rate limits)
DEFAULT_MODEL_ID=gemini-2.5-pro        # Optional: Override default model selection
LOG_LEVEL=info                         # Optional: Set logging verbosity (debug, info, warn, error)
{
  "mcpServers": {
    "GemForge": {
      "command": "node",
      "args": ["./dist/index.js"],
      "env": {
        "GEMINI_API_KEY": "your_api_key_here"
      }
    }
  }
}

GemForge は、各タスクに最適なモデルをインテリジェントに選択します。

  • gemini_search : 速度と検索の統合のためにgemini-2.5-flashを使用します

  • gemini_reason : ディープリーディング機能のためにgemini-2.5-proを使用します

  • gemini_code : 複雑なコードを理解するためにgemini-2.5-proを使用します

  • gemini_fileops : ファイルサイズに基づいてgemini-2.0-flash-liteまたはgemini-1.5-proを選択します

任意のツール呼び出しでmodel_idパラメータをオーバーライドするか、 DEFAULT_MODEL_ID環境変数を設定します。

展開

スミザリー.ai

Smithery.aiによるワンクリック展開

ドッカー

docker run -e GEMINI_API_KEY=your_api_key ghcr.io/pv-bhat/gemforge:latest

セルフホスト

統合手順については、 MCP.so ディレクトリ リストを使用してください。

GemForge の特徴は何ですか?

  • クロスエコシステムパワー：GoogleのAIとClaudeや他のMCPエージェントを連携させる

  • 複数ファイル分析: ドキュメント、画像、コードのバージョンを比較します

  • スマートルーティング: タスク要件に基づいた自動モデル選択

  • 実稼働対応：エンタープライズ環境向けに構築

GemForgeの活用

コミュニティとサポート

ドキュメント

以下のドキュメント サイトをご覧ください:

  • 高度な使用方法のチュートリアル

  • APIリファレンス

  • トラブルシューティングのヒント

ライセンス

MITライセンスに基づきます。詳細はLICENSEをご覧ください。

謝辞

Gemini APIを搭載し、 Model Context Protocolからヒントを得ています。

Available Tools

4 tools
gemini_codeC

Analyzes codebases using Repomix and Gemini 2.5 Pro. Answers questions about code structure, logic, and potential improvements.

ParametersJSON Schema
NameRequiredDescriptionDefault
codebase_pathNoPath to pre-packed Repomix file
directory_pathNoPath to the code directory
model_idNoOptional model ID override (advanced users only)
questionYesQuestion about the codebase

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool analyzes codebases and answers questions, but lacks details on permissions, rate limits, response format, or error handling. For a tool with 4 parameters and no output schema, this is a significant gap in transparency.

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 concise and front-loaded, consisting of two clear sentences that directly state the tool's function. There's no wasted verbiage, and it efficiently communicates the core purpose. However, it could be slightly more structured by explicitly mentioning key parameters or use cases.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the relationship between codebase_path and directory_path, what kind of questions are supported, or what the output looks like. For a code analysis tool with multiple input options, more context is needed to guide effective usage.

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 description doesn't explicitly discuss parameters, but schema description coverage is 100%, providing clear documentation for all 4 parameters. The description implies the tool answers questions about codebases, which aligns with the 'question' parameter. However, it doesn't add meaningful context beyond what the schema already covers, such as how codebase_path and directory_path interact.

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: 'Analyzes codebases using Repomix and Gemini 2.5 Pro. Answers questions about code structure, logic, and potential improvements.' It specifies the action (analyzes/answers), resource (codebases), and technology used (Repomix and Gemini 2.5 Pro). However, it doesn't explicitly differentiate from sibling tools like gemini_fileops, gemini_reason, or gemini_search, which likely have related but distinct functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. It mentions analyzing codebases and answering questions, but doesn't specify use cases, prerequisites, or exclusions. Without context, it's unclear how this differs from sibling tools, leaving the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

gemini_fileopsA

Performs efficient operations on files (text, PDF, images, etc.) using appropriate Gemini models (Flash-Lite or 1.5 Pro for large files). Use for summarization, extraction, or basic analysis.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYesPath to the file or array of file paths
instructionNoSpecific instruction for processing
model_idNoOptional model ID override (advanced users only)
operationNoSpecific operation type
use_large_context_modelNoSet true if the file is very large to use Gemini 1.5 Pro

TDQS

A3.5/5.0
Behavior2/5

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 model selection (Flash-Lite vs. 1.5 Pro) which adds useful context about performance characteristics, but fails to disclose critical behavioral traits such as whether operations are read-only or destructive, authentication requirements, rate limits, error handling, or output format. For a file operation tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that are front-loaded with the core purpose and usage context. Every sentence earns its place by conveying essential information without redundancy or fluff.

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 moderate complexity (5 parameters, file operations), no annotations, and no output schema, the description is incomplete. It covers the basic purpose and usage context but lacks critical behavioral details (e.g., mutation effects, error handling) and output information. The schema handles parameters well, but the description should compensate more for the missing annotations and output schema.

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?

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema by implying the 'operation' parameter corresponds to 'summarization, extraction, or basic analysis' and hinting at model selection logic, but doesn't provide additional syntax, format details, or constraints. 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'performs efficient operations on files' and specifies the types of files (text, PDF, images) and operations (summarization, extraction, basic analysis). It distinguishes from siblings by focusing on file operations rather than code, reasoning, or search. However, it doesn't specify the exact verb+resource combination beyond 'operations on files' which is slightly broad.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('for summarization, extraction, or basic analysis') and mentions model selection criteria (Flash-Lite or 1.5 Pro for large files). It doesn't explicitly state when not to use it or name alternatives among siblings, but the operational focus implies differentiation from gemini_code, gemini_reason, and gemini_search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

gemini_reasonA

Solves complex problems with step-by-step reasoning using Gemini 2.0 Flash Thinking. Best for math and science problems, coding challenges, and tasks requiring transparent reasoning process.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathNoOptional file path to include with the problem
model_idNoOptional model ID override (advanced users only)
problemYesThe complex problem or question to solve
show_stepsNoWhether to show detailed reasoning steps (default: false)

TDQS

A3.9/5.0
Behavior3/5

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 describes the step-by-step reasoning approach and mentions the 'transparent reasoning process,' which adds value beyond basic functionality. However, it doesn't cover important behavioral aspects like rate limits, authentication requirements, error handling, or what the output looks like (though there's no output schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two well-structured sentences. The first sentence states the core functionality, and the second provides usage guidance. Every word earns its place with no redundancy or unnecessary information.

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 moderate complexity (4 parameters, no output schema, no annotations), the description provides adequate but incomplete coverage. It explains the purpose and usage context well but lacks details about behavioral characteristics, output format, and error handling. With no output schema, the description should ideally mention what kind of response to expect.

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 all 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('solves complex problems with step-by-step reasoning') and resources ('using Gemini 2.0 Flash Thinking'). It distinguishes from siblings by specifying it's for 'math and science problems, coding challenges, and tasks requiring transparent reasoning process' rather than code execution, file operations, or search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about when to use this tool ('Best for math and science problems, coding challenges, and tasks requiring transparent reasoning process'), which implicitly suggests alternatives for other types of tasks. However, it doesn't explicitly name sibling tools or state when not to use this tool, keeping it at a 4 rather than a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updatesv1.0.0
    • First observedgemini_code
    • First observedgemini_fileops
    • First observedgemini_reason
    • First observedgemini_search
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: gemini_code analyzes codebases, gemini_fileops handles file operations, gemini_reason solves complex problems with reasoning, and gemini_search retrieves general information. The descriptions clearly differentiate their domains and use cases, making misselection unlikely.

Naming Consistency5/5

All tool names follow a consistent 'gemini_' prefix pattern with descriptive suffixes (code, fileops, reason, search). This uniform naming convention makes the tool set predictable and easy to understand, with no deviations in style or structure.

Tool Count4/5

Four tools is a reasonable number for a Gemini-focused server, covering key areas like code analysis, file operations, reasoning, and search. It feels slightly thin but well-scoped, as each tool addresses a distinct domain without unnecessary duplication.

Completeness4/5

The tool set covers major use cases for Gemini models: code analysis, file handling, reasoning, and information retrieval. Minor gaps might include more specialized operations like image generation or multimodal analysis, but the core functionalities are well-represented for general-purpose tasks.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

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