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GemForge-Gemini-Tools-MCP

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GemForge(제미니 도구)

대장간 배지 MCP.so

GemForge-Gemini-Tools-MCP : 선호하는 MCP 에이전트를 위한 엔터프라이즈급 Gemini 통합 솔루션입니다. 코드베이스 분석, 실시간 검색, 텍스트/PDF/이미지 처리 등의 기능으로 Claude, Roo Code, Windsurf를 더욱 강력하게 만들어 보세요.

빠른 탐색

Related MCP server: Vibe Check MCP

왜 GemForge인가요?

GemForge는 Google의 Gemini AI와 MCP 생태계를 연결하는 필수적인 다리입니다. 젬포그

  • 실시간 웹 접속 : gemini_search 사용하여 최신 뉴스, 시장 동향 및 현재 데이터를 가져옵니다.

  • 고급 추론 : gemini_reason 통해 단계별 사고로 복잡한 논리 문제를 처리합니다.

  • 코드 마스터리 : gemini_code 사용하여 전체 저장소 분석, 솔루션 생성 및 코드 디버깅

  • 다중 파일 처리 : gemini_fileops 사용하여 PDF, 이미지 등 60개 이상의 파일 형식을 처리합니다.

  • 지능형 모델 선택 : 각 작업에 맞는 최적의 Gemini 모델로 자동 라우팅

  • 엔터프라이즈 지원 : 강력한 오류 처리, 속도 제한 관리 및 API 대체 메커니즘

빠른 시작

한 줄 설치

지엑스피1

수동 설정

  1. 구성 파일( claude_desktop_config.json )을 생성합니다.

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

  1. 설치하고 실행하세요:

npm install gemforge-mcp
npm start

30초 설정 데모 보기 →

견고한 신뢰성

GemForge는 프로덕션 환경을 위해 구축되었습니다.

  • 60개 이상의 파일 유형 지원 : 코드부터 문서, 이미지까지 모든 것을 처리합니다.

  • 자동 모델 폴백 : 요금 제한이나 서비스 중단 중에도 계속 작동합니다.

  • 엔터프라이즈급 오류 로깅 : 문제 해결을 위한 자세한 진단

  • API 복원력 : 지수적 백오프, 재시도 논리 및 원활한 모델 전환

  • 전체 저장소 지원 : 구성 가능한 포함/제외 패턴을 사용하여 전체 코드베이스 분석

  • XML 콘텐츠 처리 : 구조화된 데이터에 대한 특수 처리

주요 도구

도구

설명

핵심 역량

gemini_search

웹 연결 정보 검색

실시간 데이터 접근

gemini_reason

단계별 논리로 복잡한 문제 해결

투명한 추론 과정

gemini_code

심층적인 코드 이해 및 생성

전체 저장소 분석

gemini_fileops

60개 이상의 형식에 걸친 다중 파일 처리

문서 비교 및 변환

{
  "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."
  }
}

구성

GemForge는 유연한 구성 옵션을 제공합니다.

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"
      }
    }
  }
}

GemForge는 각 작업에 가장 적합한 모델을 지능적으로 선택합니다.

  • gemini_search : 속도 및 검색 통합을 위해 gemini-2.5-flash 사용합니다.

  • gemini_reason : 심층 추론 기능을 위해 gemini-2.5-pro 사용합니다.

  • gemini_code : 복잡한 코드 이해를 위해 gemini-2.5-pro 사용합니다.

  • gemini_fileops : 파일 크기에 따라 gemini-2.0-flash-lite 또는 gemini-1.5-pro 중에서 선택합니다.

모든 도구 호출에서 model_id 매개변수를 재정의하거나 DEFAULT_MODEL_ID 환경 변수를 설정합니다.

전개

스미서리.ai

Smithery.ai를 통한 원클릭 배포

도커

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

셀프 호스팅

통합 지침은 MCP.so 디렉토리 목록을 참조하세요.

GemForge의 차별점은 무엇인가?

  • 크로스 에코시스템 파워 : Google의 AI와 Claude 및 기타 MCP 에이전트 연결

  • 다중 파일 분석 : 문서, 이미지 또는 코드 버전 비교

  • 스마트 라우팅 : 작업 요구 사항에 따른 자동 모델 선택

  • 프로덕션 준비 완료 : 기업 환경을 위해 제작됨

GemForge의 실제 활용

커뮤니티 및 지원

선적 서류 비치

다음 내용은 문서 사이트에서 확인하세요.

  • 고급 사용법 튜토리얼

  • API 참조

  • 문제 해결 팁

특허

MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스를 참조하세요.

감사의 말

Gemini API를 기반으로 하며 Model Context Protocol 에서 영감을 받았습니다.

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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