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GemForge-Gemini-Tools-MCP

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GemForge（Gemini 工具）

锻造徽章 MCP文件

GemForge-Gemini-Tools-MCP ：企业级 Gemini 集成，适用于您常用的 MCP 代理。增强 Claude、Roo Code 和 Windsurf 的功能，提供代码库分析、实时搜索、文本/PDF/图像处理等功能。

快速导航

Related MCP server: Vibe Check MCP

为什么选择 GemForge？

GemForge 是 Google Gemini AI 与 MCP 生态系统之间的重要桥梁： 宝石雾

  • 实时网络访问：使用gemini_search获取突发新闻、市场趋势和当前数据

  • 高级推理：通过gemini_reason逐步思考处理复杂的逻辑问题

  • 代码精通：使用gemini_code分析完整存储库、生成解决方案和调试代码

  • 多文件处理：使用gemini_fileops处理 60 多种文件格式，包括 PDF、图像等

  • 智能模型选择：自动为每个任务路由到最佳的 Gemini 模型

  • 企业级：强大的错误处理、速率限制管理和 API 回退机制

快速入门

单行安装

npx @gemforge/mcp-server@latest init

手动设置

  1. 创建配置文件（ claude_desktop_config.json ）：

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

  1. 安装并运行：

npm install gemforge-mcp
npm start

观看 30 秒设置演示 →

重载可靠性

GemForge 专为生产环境而构建：

  • 支持 60 多种文件类型：处理从代码到文档到图像的所有内容

  • 自动模型回退：即使在速率限制或服务中断期间也能继续运行

  • 企业级错误日志：详细的诊断，用于故障排除

  • API 弹性：指数退避、重试逻辑和无缝模型切换

  • 完整的存储库支持：使用可配置的包含/排除模式分析整个代码库

  • XML内容处理：针对结构化数据的专门处理

关键工具

工具

描述

关键能力

gemini_search

网络连接信息检索

实时数据访问

gemini_reason

通过逐步逻辑解决复杂问题

透明的推理过程

gemini_code

深度代码理解和生成

完整的存储库分析

gemini_fileops

跨 60 多种格式的多文件处理

文档比较与转换

{
  "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."
  }
}

配置

GemForge 提供灵活的配置选项：

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 为每个任务智能选择最佳模型：

  • gemini_search ：使用gemini-2.5-flash来提高速度并进行搜索集成

  • gemini_reason ：使用gemini-2.5-pro实现深度推理能力

  • gemini_code ：使用gemini-2.5-pro来理解复杂的代码

  • gemini_fileops ：根据文件大小在gemini-2.0-flash-lite或gemini-1.5-pro之间进行选择

在任何工具调用中使用model_id参数覆盖或设置DEFAULT_MODEL_ID环境变量。

部署

Smithery.ai

通过Smithery.ai一键部署

Docker

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

自托管

使用我们的MCP.so 目录列表获取集成说明。

GemForge 有何独特之处？

  • 跨生态系统的力量：将谷歌的人工智能与 Claude 和其他 MCP 代理连接起来

  • 多文件分析：比较文档、图像或代码版本

  • 智能路由：根据任务需求自动选择模型

  • 生产就绪：专为企业环境打造

GemForge 实际应用

社区与支持

文档

请访问我们的文档网站以获取：

  • 高级使用教程

  • API 参考

  • 故障排除提示

执照

根据 MIT 许可证授权。详情请参阅许可证。

致谢

由Gemini API提供支持并受到模型上下文协议的启发。

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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