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mentor-mcp-server

by cyanheads
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TypeScript
Remote

メンターMCPサーバー

タイプスクリプト モデルコンテキストプロトコルバージョンライセンス状態 GitHub

モデルコンテキストプロトコルサーバーは、AIを活用したDeepseek推論（R1）メンターシップ機能を通じてLLMエージェントにセカンドオピニオンを提供します。これには、コードレビュー、設計批評、ライティングフィードバック、Deepseek APIを介したアイデアブレインストーミングなどが含まれます。専門家によるセカンドオピニオンと実用的な洞察を活用して、LLMエージェントを成功へと導きます。

モデルコンテキストプロトコル

モデル コンテキスト プロトコル (MCP) は、次の間の通信を可能にします。

  • クライアント: Claude Desktop、IDE、およびその他の MCP 互換クライアント

  • サーバー: タスク管理と自動化のためのツールとリソース

  • LLMエージェント:サーバーの機能を活用するAIモデル

Related MCP server: Senior Consult MCP

目次

特徴

コード分析

  • 包括的なコードレビュー

  • バグの検出と防止

  • スタイルとベストプラクティスの評価

  • パフォーマンス最適化の提案

  • セキュリティ脆弱性評価

デザインと建築

  • UI/UXデザイン批評

  • アーキテクチャ図分析

  • デザインパターンの推奨事項

  • アクセシビリティ評価

  • 一貫性チェック

コンテンツの強化

  • ライティングのフィードバックと改善

  • 文法とスタイルの分析

  • ドキュメントレビュー

  • コンテンツの明確さの評価

  • 構造に関する推奨事項

戦略計画

  • 機能強化のブレインストーミング

  • アプローチに関するセカンドオピニオン

  • イノベーションの提案

  • 実現可能性分析

  • ユーザー価値評価

インストール

# Clone the repository
git clone git@github.com:cyanheads/mentor-mcp-server.git
cd mentor-mcp-server

# Install dependencies
npm install

# Build the project
npm run build

構成

MCP クライアント設定に追加:

{
  "mcpServers": {
    "mentor": {
      "command": "node",
      "args": ["build/index.js"],
      "env": {
        "DEEPSEEK_API_KEY": "your_api_key",
        "DEEPSEEK_MODEL": "deepseek-reasoner",
        "DEEPSEEK_MAX_TOKENS": "8192",
        "DEEPSEEK_MAX_RETRIES": "3",
        "DEEPSEEK_TIMEOUT": "30000"
      }
    }
  }
}

環境変数

変数

必須

デフォルト

説明

ディープシークAPIキー

はい

-

Deepseek APIキー

ディープシークモデル

はい

ディープシーク推論器

Deepseekモデル名

ディープシーク_最大_トークン

いいえ

8192

リクエストあたりの最大トークン数

ディープシーク_MAX_RETRIES

いいえ

3

再試行回数

ディープシークタイムアウト

いいえ

30000

リクエストタイムアウト（ミリ秒）

ツール

コードレビュー

<use_mcp_tool>
<server_name>mentor-mcp-server</server_name>
<tool_name>code_review</tool_name>
<arguments>
{
  "file_path": "src/app.ts",
  "language": "typescript"
}
</arguments>
</use_mcp_tool>

デザイン批評

<use_mcp_tool>
<server_name>mentor-mcp-server</server_name>
<tool_name>design_critique</tool_name>
<arguments>
{
  "design_document": "path/to/design.fig",
  "design_type": "web UI"
}
</arguments>
</use_mcp_tool>

ライティングフィードバック

<use_mcp_tool>
<server_name>mentor-mcp-server</server_name>
<tool_name>writing_feedback</tool_name>
<arguments>
{
  "text": "Documentation content...",
  "writing_type": "documentation"
}
</arguments>
</use_mcp_tool>

機能強化

<use_mcp_tool>
<server_name>mentor-mcp-server</server_name>
<tool_name>brainstorm_enhancements</tool_name>
<arguments>
{
  "concept": "User authentication system"
}
</arguments>
</use_mcp_tool>

例

各ツールの使用法と出力の詳細な例は、 examplesディレクトリにあります。

各例には、ツールの機能と出力構造を示すリクエスト形式とサンプル応答が含まれています。

発達

# Build TypeScript code
npm run build

# Start the server
npm run start

# Development with watch mode
npm run dev

# Clean build artifacts
npm run clean

プロジェクト構造

src/
├── api/         # API integration modules
├── tools/       # Tool implementations
│   ├── second-opinion/
│   ├── code-review/
│   ├── design-critique/
│   ├── writing-feedback/
│   └── brainstorm-enhancements/
├── types/       # TypeScript type definitions
├── utils/       # Utility functions
├── config.ts    # Server configuration
├── index.ts     # Entry point
└── server.ts    # Main server implementation

ライセンス

Apache License 2.0。詳細についてはLICENSEを参照してください。

Available Tools

5 tools
brainstorm_enhancementsC

Generates creative ideas for improving a given concept, product, or feature, focusing on innovation, feasibility, and user value.

ParametersJSON Schema
NameRequiredDescriptionDefault
conceptYesA description of the concept, product, or feature to enhance

TDQS

C2.9/5.0
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 the tool 'generates' ideas and focuses on certain criteria, but doesn't describe output format, potential limitations (e.g., idea count, quality), or any side effects like rate limits or authentication needs. This leaves significant gaps for a tool that produces creative content.

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 a single, efficient sentence that front-loads the core action and purpose without any wasted words. Every part of the sentence contributes to understanding the tool's function and focus areas.

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 lack of annotations and output schema, the description is incomplete for a creative generation tool. It doesn't explain what the output looks like (e.g., list of ideas, structured format), how many ideas are generated, or any behavioral constraints, leaving the agent with insufficient context for effective use.

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%, with the single parameter 'concept' well-documented in the schema. The description adds marginal value by reiterating that the concept is for 'enhancing' and specifying it can be a 'concept, product, or feature', but doesn't provide additional syntax or format details beyond what the schema already covers.

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 with specific verbs ('Generates creative ideas for improving') and resources ('concept, product, or feature'), and specifies the focus areas ('innovation, feasibility, and user value'). However, it doesn't explicitly differentiate from sibling tools like 'design_critique' or 'second_opinion', which might also involve improvement suggestions.

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 like 'design_critique' or 'second_opinion', nor does it mention any prerequisites or exclusions. It implies usage for enhancement ideas but lacks explicit context for tool selection.

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

code_reviewC

Provides a code review for a given file or code snippet, focusing on potential bugs, style issues, performance bottlenecks, and security vulnerabilities.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathNoThe full path to the local file containing the code to review
languageNoThe programming language of the code
code_snippetNoOptional small code snippet for quick reviews (alternative to file_path)

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the review focuses on (bugs, style, performance, security) but doesn't describe the output format, depth of analysis, whether it modifies code, authentication needs, rate limits, or error handling. For a tool with no annotations, this leaves significant behavioral gaps.

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 a single, efficient sentence that front-loads the core purpose and lists key focus areas. Every word earns its place with zero redundancy or wasted text. It's appropriately sized for this tool's complexity.

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 no annotations and no output schema, the description is incomplete for this tool's complexity. It doesn't explain what the review output looks like (structured report? list of issues?), depth of analysis, or limitations. For a code review tool with 3 parameters and no structured output documentation, the description should provide more contextual information.

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 three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain trade-offs between file_path vs code_snippet, or language-specific considerations). Baseline 3 is appropriate when 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: 'Provides a code review' with specific focus areas (bugs, style, performance, security). It uses a specific verb ('Provides') and resource ('code review'), but doesn't explicitly differentiate from sibling tools like 'design_critique' or 'second_opinion' which might overlap in scope.

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 doesn't mention when to choose 'code_review' over 'design_critique' or 'second_opinion', nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name alone.

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

design_critiqueB

Offers a critique of a design document, UI/UX mockup, or architectural diagram, focusing on usability, aesthetics, consistency, accessibility, and potential design flaws.

ParametersJSON Schema
NameRequiredDescriptionDefault
design_documentYesA description or URL to the design document/image
design_typeYesType of design (e.g., 'web UI', 'system architecture', 'mobile app')

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the tool 'offers a critique' but doesn't disclose behavioral traits such as output format, depth of analysis, whether it's automated or human-like, potential limitations, or how it handles different design types. For a critique tool with zero annotation coverage, this leaves significant gaps in understanding its operation.

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 a single, efficient sentence that front-loads the purpose and key details without waste. It clearly states what the tool does, the input types, and focus areas, making it easy to parse and understand quickly.

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 complexity of a critique tool (which could involve subjective analysis), no annotations, no output schema, and 2 parameters with full schema coverage, the description is incomplete. It doesn't explain what the critique output looks like, any limitations, or how it integrates with sibling tools. For a tool that provides feedback, more context on behavior and results is needed.

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 both parameters ('design_document' and 'design_type') with descriptions. The description adds no additional meaning beyond what the schema provides, such as examples or constraints for parameter 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.

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: 'Offers a critique' of design artifacts, specifying the types (document, mockup, diagram) and focus areas (usability, aesthetics, consistency, accessibility, flaws). It distinguishes from siblings like 'brainstorm_enhancements' by focusing on critique rather than ideation, but doesn't explicitly name alternatives. This is clear but lacks explicit sibling differentiation.

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 implies usage context through the specified design types and focus areas, suggesting it's for evaluating design quality. However, it doesn't explicitly state when to use this tool versus alternatives like 'second_opinion' (which might overlap) or 'code_review' (for code). No guidance on prerequisites or exclusions is provided.

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

second_opinionC

Provides a second opinion on a user's request by analyzing it with an LLM and listing critical considerations.

ParametersJSON Schema
NameRequiredDescriptionDefault
user_requestYesThe user's original request (e.g., 'Explain Python to me' or 'Build a login system')

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 uses an LLM and lists critical considerations, but doesn't describe important traits like whether it's read-only or has side effects, what format the output takes, potential rate limits, or authentication needs. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that clearly states the tool's function. It's appropriately sized for a simple tool with one parameter, though it could potentially be more front-loaded with additional context about when to use it. There's no wasted verbiage or redundancy.

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 (analyzing requests with LLM), lack of annotations, and no output schema, the description is minimally adequate but has clear gaps. It explains what the tool does but doesn't cover behavioral aspects, usage context, or output format. For a tool that presumably returns LLM-generated analysis, more detail about the nature of the 'critical considerations' would be helpful.

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%, with the single parameter 'user_request' well-documented in the schema. The description doesn't add any meaningful information about parameters beyond what the schema already provides (e.g., it doesn't clarify what constitutes a valid 'user_request' or provide examples beyond those in the schema). With high schema coverage, the baseline score of 3 is appropriate.

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: 'Provides a second opinion on a user's request by analyzing it with an LLM and listing critical considerations.' It specifies the action (provides second opinion), method (analyzing with LLM), and output (listing critical considerations). However, it doesn't explicitly differentiate from sibling tools like 'design_critique' or 'writing_feedback' which might also provide analytical feedback.

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 offers no guidance on when to use this tool versus alternatives. With sibling tools like 'brainstorm_enhancements', 'code_review', 'design_critique', and 'writing_feedback' available, there's no indication of what makes 'second_opinion' distinct or when it's the appropriate choice. The description implies usage for analyzing user requests but doesn't specify context or exclusions.

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

writing_feedbackC

Provides feedback on a piece of writing, such as an essay, article, or technical documentation, focusing on clarity, grammar, style, structure, and overall effectiveness.

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesThe text to review
writing_typeYesThe type of writing (e.g., 'essay', 'article', 'documentation')

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it states what the tool does (provides feedback), it doesn't describe how it behaves: no information about response format, depth of analysis, whether it's generative or evaluative, processing time, or any limitations. This is inadequate for a tool with no annotation coverage.

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 concise - a single sentence that efficiently communicates the core functionality. It's front-loaded with the main purpose and includes relevant examples. There's no wasted verbiage or redundant information.

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 no annotations and no output schema, the description is incomplete. For a feedback tool with 2 parameters, it should explain what kind of feedback to expect, response format, or any constraints. The description covers what the tool does but not how it works or what it returns, leaving significant gaps for agent understanding.

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 both parameters fully. The description adds no additional parameter semantics beyond what's in the schema - it mentions writing types but doesn't elaborate on format expectations, length constraints, or special requirements. Baseline 3 is appropriate when 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: 'Provides feedback on a piece of writing' with specific focus areas (clarity, grammar, style, structure, effectiveness). It distinguishes from sibling tools like code_review and design_critique by specifying writing domains (essay, article, technical documentation). However, it doesn't explicitly differentiate from second_opinion which could also provide feedback.

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 doesn't mention when to choose writing_feedback over brainstorm_enhancements, code_review, design_critique, or second_opinion. There are no explicit when/when-not statements or alternative recommendations.

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. 5 tool updatesv1.0.0
    • First observedbrainstorm_enhancements
    • First observedcode_review
    • First observeddesign_critique
    • First observedsecond_opinion
    • First observedwriting_feedback
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

A3.5/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose targeting different domains: brainstorming, code review, design critique, second opinions, and writing feedback. There is no overlap in functionality, making it easy for an agent to select the appropriate tool without confusion.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a clear verb_noun structure (e.g., brainstorm_enhancements, code_review). This predictability enhances usability and aligns with common MCP conventions.

Tool Count5/5

With 5 tools, the server is well-scoped for its mentoring/feedback purpose. Each tool serves a unique and valuable function, avoiding bloat while covering key areas like code, design, writing, and idea generation.

Completeness4/5

The toolset covers major feedback domains (code, design, writing, brainstorming) and includes a general second_opinion tool. A minor gap is the lack of a tool for project management or strategic planning feedback, but core mentoring workflows are well-supported.

Maintenance

ActivityInactive
ResponsivenessNo issues

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