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mcp-neurolora

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MCP ニューロロラ

MCPサーバーバージョンライセンス

OpenAI API を使用したコード分析、コード収集、ドキュメント生成のためのツールを提供するインテリジェントな MCP サーバー。

🚀 インストールガイド

まだ何もインストールしていなくてもご心配なく！以下の手順に従うか、アシスタントにインストールを依頼してください。

ステップ1: Node.jsをインストールする

macOS

  1. Homebrew がインストールされていない場合はインストールします。

    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

  2. Node.js 18をインストールします。

    brew install node@18
echo 'export PATH="/opt/homebrew/opt/node@18/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

ウィンドウズ

  1. nodejs.orgからNode.js 18 LTSをダウンロードしてください。

  2. インストーラーを実行する

  3. 変更を適用するには新しいターミナルを開きます

Linux（Ubuntu/Debian）

curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
sudo apt-get install -y nodejs

ステップ2: uvとuvxをインストールする

すべてのオペレーティングシステム

  1. uvをインストールします:

    curl -LsSf https://astral.sh/uv/install.sh | sh

  2. uvxをインストールします:

    uv pip install uvx

ステップ3: インストールの確認

すべてがインストールされていることを確認するには、次のコマンドを実行します。

node --version  # Should show v18.x.x
npm --version   # Should show 9.x.x or higher
uv --version    # Should show uv installed
uvx --version   # Should show uvx installed

ステップ4: MCPサーバーを構成する

アシスタントがお手伝いします:

  1. Cline 設定ファイルを見つけます:

    • VSCode: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

    • Claude デスクトップ: ~/Library/Application Support/Claude/claude_desktop_config.json

    • Windows VSCode: %APPDATA%/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

    • Windows クロード: %APPDATA%/Claude/claude_desktop_config.json

  2. 次の構成を追加します。

    {
  "mcpServers": {
    "aindreyway-mcp-neurolora": {
      "command": "npx",
      "args": ["-y", "@aindreyway/mcp-neurolora@latest"],
      "env": {
        "NODE_OPTIONS": "--max-old-space-size=256",
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

ステップ5: ベースサーバーのインストール

アシスタントに「私の環境用のベース MCP サーバーをインストールしてください」と依頼するだけです。

アシスタントは次のことを行います:

  1. 設定ファイルを見つける

  2. install_base_serversツールを実行する

  3. 必要なサーバーをすべて自動的に構成する

インストールが完了したら:

  1. VSCode を完全に閉じます (macOS では Cmd+Q、Windows では Alt+F4)

  2. VSCodeを再度開く

  3. 新しいサーバーはすぐに使用可能になります

**重要:**ベース サーバーをインストールした後、適切に初期化するには VSCode を完全に再起動する必要があります。

**注:**このサーバーは、npm パッケージを直接実行するためにnpxを使用します。これは、Node.js/TypeScript MCP サーバーに最適であり、npm エコシステムと TypeScript ツールとのシームレスな統合を提供します。

Related MCP server: Code Context Provider MCP

ベースMCPサーバー

次のベース サーバーが自動的にインストールおよび構成されます。

  • フェッチ: Webリソースにアクセスするための基本的なHTTPリクエスト機能

  • puppeteer: Webインタラクションとテストのためのブラウザ自動化機能

  • シーケンシャルシンキング：複雑なタスクのための高度な問題解決ツール

  • github: リポジトリ管理のためのGitHub統合機能

  • git: バージョン管理のための Git 操作のサポート

  • シェル: 一般的なコマンドを使用した基本的なシェルコマンドの実行:

    • ls: ディレクトリの内容を一覧表示する

    • cat: ファイルの内容を表示する

    • pwd: 作業ディレクトリを印刷する

    • grep: テキストパターンを検索する

    • wc: 単語、行、文字数を数える

    • タッチ: 空のファイルを作成する

    • find: ファイルを検索する

🎯 アシスタントでできること

アシスタントに次のことを依頼します。

  • 「私のコードを分析して改善を提案してください」

  • 「自分の環境にベース MCP サーバーをインストールする」

  • 「プロジェクトディレクトリからコードを収集する」

  • 「コードベースのドキュメントを作成する」

  • 「すべてのコードを含むマークダウンファイルを生成する」

🛠 利用可能なツール

コードを分析する

OpenAI API を使用してコードを分析し、改善提案を含む詳細なフィードバックを生成します。

パラメータ:

  • codePath (必須): 分析するコードファイルまたはディレクトリへのパス

使用例:

{
  "codePath": "/path/to/your/code.ts"
}

このツールは次のことを行います。

  1. OpenAI APIを使用してコードを分析する

  2. 次の方法で詳細なフィードバックを生成します:

    • 問題点と提言

    • ベストプラクティス違反

    • 影響分析

    • 修正手順

  3. プロジェクトに 2 つの出力ファイルを作成します。

    • LAST_RESPONSE_OPENAI.txt - 人間が読める分析

    • LAST_RESPONSE_OPENAI_GITHUB_FORMAT.json - GitHub の問題の構造化データ

注: 環境設定に OpenAI API キーが必要です

収集コード

ディレクトリ内のすべてのコードを、構文の強調表示とナビゲーションを備えた単一のマークダウン ファイルに収集します。

パラメータ:

  • directory （必須）: コードを収集するディレクトリパス

  • outputPath (オプション): 出力マークダウンファイルを保存するパス

  • ignorePatterns (オプション): 無視するパターンの配列 (.gitignore と同様)

使用例:

{
  "directory": "/path/to/project/src",
  "outputPath": "/path/to/project/src/FULL_CODE_SRC_2024-12-20.md",
  "ignorePatterns": ["*.log", "temp/", "__pycache__", "*.pyc", ".git"]
}

インストールベースサーバー

基本 MCP サーバーを構成ファイルにインストールします。

パラメータ:

  • configPath (必須): MCP設定構成ファイルへのパス

使用例:

{
  "configPath": "/path/to/cline_mcp_settings.json"
}

🔧 機能

サーバーは以下を提供します:

  • コード分析:

    • OpenAI API統合

    • 構造化されたフィードバック

    • ベストプラクティスの推奨事項

    • GitHub の問題生成

  • コードコレクション:

    • ディレクトリトラバーサル

    • 構文の強調表示

    • ナビゲーション生成

    • パターンベースのフィルタリング

  • ベースサーバー管理:

    • 自動インストール

    • 構成処理

    • バージョン管理

📄 ライセンス

MIT ライセンス - プロジェクトで自由にご利用ください。

👤 著者

エインドリーウェイ

⭐️ サポート

このプロジェクトが役に立った場合は ⭐️ を付けてください!

Available Tools

4 tools
analyze_codeC

Analyze code using OpenAI API (requires your API key). The analysis may take a few minutes. So, wait please.

ParametersJSON Schema
NameRequiredDescriptionDefault
codePathYesAbsolute path to the code file to analyze (e.g. /Users/username/project/src/code.ts)

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that analysis 'may take a few minutes' (timing behavior) and requires an API key (authentication need), which are useful behavioral traits. However, it lacks details on rate limits, error handling, or what happens during analysis (e.g., data sent to OpenAI). No contradiction with annotations exists.

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 with three short sentences, front-loading the main action ('Analyze code using OpenAI API'). However, the phrase 'So, wait please' is slightly informal and could be more structured, though it efficiently conveys timing without unnecessary details.

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 no annotations, no output schema, and a simple input schema, the description provides basic context (purpose, timing, auth) but is incomplete. It doesn't explain what the analysis returns (e.g., insights, reports) or potential limitations, leaving gaps for a tool that interacts with an external API and involves waiting.

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%, with the parameter 'codePath' well-documented in the schema as an absolute path. The description adds no additional meaning beyond this, such as file format constraints or analysis scope based on path. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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

Purpose3/5

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

The description states the tool 'Analyze code using OpenAI API' which provides a verb ('analyze') and resource ('code'), but it's vague about what analysis entails (e.g., security, performance, style). It doesn't distinguish from siblings like 'collect_code' or 'create_github_issues', leaving ambiguity in purpose.

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 mentions 'requires your API key' and 'wait please', implying prerequisites and timing, but offers no explicit guidance on when to use this tool versus alternatives like 'collect_code' for gathering code or 'create_github_issues' for issue tracking. No exclusions or clear context for tool selection are provided.

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

collect_codeC

Collect all code from a directory into a single markdown file

ParametersJSON Schema
NameRequiredDescriptionDefault
ignorePatternsNoPatterns to ignore (similar to .gitignore)
inputYes
outputPathYesPath where to save the output markdown file

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 action ('collect') and output format ('markdown file'), but fails to detail critical behaviors like whether it overwrites existing files, handles errors (e.g., missing directories), requires specific permissions, or includes metadata in the output. This leaves significant gaps for a tool that modifies files.

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 without unnecessary details. It uses clear language ('collect all code', 'single markdown file') and avoids redundancy, making it easy to parse 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 tool's complexity (file system operations, output generation) and lack of annotations or output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, file overwriting, or output structure, which are crucial for safe and effective use. This inadequacy is notable for a tool that creates files.

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 adds minimal parameter semantics beyond the schema, which has 67% coverage. It implies 'input' is for source paths and 'outputPath' for the markdown file, but doesn't explain the dual nature of 'input' (directory vs. list) or how 'ignorePatterns' functions in practice. With moderate schema coverage, the baseline is 3, as the description doesn't fully compensate for the gaps.

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 action ('collect all code') and the output ('into a single markdown file'), specifying both verb and resource. However, it doesn't explicitly differentiate from sibling tools like 'analyze_code' or 'create_github_issues', which might involve code handling but serve different purposes.

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 'analyze_code' for code analysis or 'create_github_issues' for issue tracking. It lacks context about prerequisites, such as needing access to the directory, or exclusions, like not being suitable for real-time code processing.

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

create_github_issuesC

Create GitHub issues from analysis results. Requires GitHub token.

ParametersJSON Schema
NameRequiredDescriptionDefault
issueNumbersNoIssue numbers to create (optional, creates all issues if not specified)
ownerYesGitHub repository owner
repoYesGitHub repository name

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 requirement for a GitHub token, which adds some context about authentication needs. However, it fails to describe key behavioral traits such as whether this is a write operation (implied by 'create' but not explicit), potential side effects, error handling, rate limits, or what the output looks like. For a mutation 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.

Conciseness4/5

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

The description is concise with two short sentences that are front-loaded with the main purpose. There's no unnecessary verbosity, and each sentence serves a purpose: the first states the action, and the second adds a critical requirement. However, it could be slightly more structured by explicitly separating purpose from prerequisites.

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 as a write operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects like mutation effects, error cases, and output format. While it mentions a token requirement, it doesn't cover other contextual needs such as permissions or integration with sibling tools, leaving gaps for the agent to infer.

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%, meaning the input schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as clarifying the relationship between 'issueNumbers' and 'analysis results' or providing examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 action ('create GitHub issues') and the source ('from analysis results'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'analyze_code' or 'collect_code' which might also relate to code analysis workflows, leaving room for ambiguity about when to use this versus other tools in the server.

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 mentions 'Requires GitHub token' which is a prerequisite but not a usage guideline. It provides no guidance on when to use this tool versus alternatives like 'analyze_code' or 'collect_code', nor does it specify scenarios where this tool is appropriate or inappropriate. Without such context, the agent lacks direction on tool selection.

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

install_base_serversC

Install base MCP servers to the configuration

ParametersJSON Schema
NameRequiredDescriptionDefault
configPathYesPath to the MCP settings configuration file

TDQS

C2.6/5.0
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 states the tool performs an installation, implying a write/mutation operation, but fails to describe critical behaviors such as whether it overwrites existing configurations, requires specific permissions, or has side effects like restarting services. This leaves significant gaps in understanding the tool's impact.

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, straightforward sentence that efficiently conveys the core action without unnecessary words. It is appropriately sized for a simple tool, though it could be more front-loaded with additional context to improve clarity.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on what 'base MCP servers' entail, the outcome of the installation, error conditions, or how it interacts with the configuration file. This leaves the agent with insufficient information to use the tool effectively in complex scenarios.

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 input schema has 100% description coverage, clearly documenting the single parameter 'configPath'. The description does not add any meaning beyond what the schema provides, as it mentions no parameters. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra help from the description.

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

Purpose3/5

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

The description states the action ('Install') and target ('base MCP servers'), but it's vague about what 'base MCP servers' specifically are and doesn't distinguish this from sibling tools like analyze_code or collect_code. It provides a basic purpose but lacks specificity and differentiation.

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?

No guidance is provided on when to use this tool versus alternatives or in what context it should be applied. The description does not mention prerequisites, timing, or exclusions, leaving the agent with no usage instructions beyond the basic action.

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 observedanalyze_code
    • First observedcollect_code
    • First observedcreate_github_issues
    • First observedinstall_base_servers
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

B3/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: analyze_code for code analysis, collect_code for file aggregation, create_github_issues for issue creation, and install_base_servers for server installation. The descriptions clearly differentiate their functions, eliminating any ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., analyze_code, collect_code, create_github_issues, install_base_servers). The naming is uniform and predictable, using snake_case throughout with clear action-object pairs.

Tool Count3/5

With only 4 tools, the set feels thin for a server named 'mcp-neurolora', which suggests a broader scope related to code analysis or AI workflows. While the tools cover specific tasks, the count is borderline low, potentially leaving gaps in functionality for the implied domain.

Completeness2/5

The tool set has significant gaps for a code analysis or AI workflow server. It lacks core operations like retrieving or updating issues, managing analysis results, or handling configurations beyond installation. This incomplete surface will likely cause agent failures in extended workflows.

Resources

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