MCP Reasoner
MCP推論者
ビームサーチとモンテカルロツリーサーチ (MCTS) の両方の機能を備えた、Claude Desktop 用の体系的推論 MCP サーバー実装。
特徴
二重の検索戦略:
設定可能な幅のビームサーチ
複雑な意思決定空間のためのMCTS
思考のスコアリングと評価
ツリーベース推論パス
推論プロセスの統計分析
MCPプロトコル準拠
Related MCP server: Sequential Thinking MCP Server
インストール
git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run build構成
Claude Desktop 設定に追加:
{
"mcpServers": {
"mcp-reasoner": {
"command": "node",
"args": ["path/to/mcp-reasoner/dist/index.js"],
}
}
}検索戦略
ビームサーチ
最も有望なパスの固定幅セットを維持する
ステップバイステップの推論に最適
最適な用途: 数学の問題、論理パズル
モンテカルロ木探索
シミュレーションに基づく意思決定空間の探索
探索と開発のバランスをとる
最適な用途: 結果が不確実な複雑な問題
**注:**モンテカルロ木探索により、クロードはArc AGIベンチマークで非常に優れたパフォーマンスを発揮しました(公開テストでは6/10点)。一方、ビーム探索では同じパズルで3/10点という結果でした。非常に複雑なタスクでは、ビーム探索ではなくMCTS戦略を使用するようにクロードに指示する必要があります。
アルゴリズムの詳細
検索戦略の選択
ビームサーチ: 複数のソリューションパスを評価してランク付けする
MCTS: ノード選択とランダムロールアウトに UCT を使用する
思考スコアの基準:
詳細レベル
数式
論理コネクタ
親子関係の強さ
プロセス管理
ツリーベースの状態追跡
推論の統計分析
進捗状況の監視
ユースケース
数学の問題
論理パズル
ステップバイステップの分析
複雑な問題の分解
決定木探索
戦略の最適化
将来の実装
新しいアルゴリズムを実装する
反復深化深さ優先探索(IDDFS)
アルファベータ剪定
ライセンス
このプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細についてはLICENSEファイルを参照してください。
Available Tools
1 toolmcp-reasonerC
Advanced reasoning tool with multiple strategies including Beam Search and Monte Carlo Tree Search
| Name | Required | Description | Default |
|---|---|---|---|
| nextThoughtNeeded | Yes | Whether another step is needed | |
| strategyType | No | Reasoning strategy to use (beam_search or mcts) | |
| thought | Yes | Current reasoning step | |
| thoughtNumber | Yes | Current step number | |
| totalThoughts | Yes | Total expected steps |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'Advanced reasoning' and strategies, but doesn't disclose behavioral traits such as whether it's read-only or destructive, performance characteristics, error handling, or output format. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key information ('Advanced reasoning tool') and includes strategy examples. It avoids unnecessary details, but could be slightly more structured by explicitly stating the tool's output or use case to improve clarity without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a reasoning tool with multiple strategies and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., reasoning results, next steps), how strategies affect outcomes, or any limitations. With no annotations and rich parameters, more context is needed for effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining the relationship between thought steps or strategy implications. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states this is an 'Advanced reasoning tool with multiple strategies' which provides a general purpose, but it's vague about what specific reasoning it performs (e.g., problem-solving, decision-making) and lacks a clear verb+resource combination. It mentions strategies like Beam Search and Monte Carlo Tree Search, which gives some context but doesn't specify the domain or output of the reasoning process.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, as no sibling tools are listed, and the description doesn't provide context for its application (e.g., for complex problems, iterative reasoning). It implies usage through strategy mentions but lacks explicit when/when-not instructions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The tool's purpose is clearly defined as an advanced reasoning tool with multiple strategies.
A single tool inherently has perfect naming consistency, as there is only one name to consider. The tool name 'mcp-reasoner' follows a clear pattern and does not conflict with any other tool names.
A single tool is generally too few for most server purposes, as it limits functionality and scope. While it might be appropriate for a highly specialized server, it often feels thin and incomplete for broader use cases.
With only one tool, the surface is severely incomplete. There are no other operations to support a full reasoning workflow, such as configuring strategies, retrieving results, or managing sessions, leading to significant gaps in functionality.
Maintenance
Resources
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