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parmarjh

MCP Reasoner

by parmarjh

MCP 리즈너

Claude Desktop을 위한 체계적 추론 MCP 서버 구현으로, 빔 검색과 몬테카를로 트리 검색(MCTS) 기능을 모두 갖추고 있습니다.

특징

  • 이중 검색 전략:

    • 구성 가능한 폭을 갖춘 빔 검색

    • 복잡한 의사결정 공간을 위한 MCTS

  • 생각 채점 및 평가

  • 트리 기반 추론 경로

  • 추론 과정의 통계적 분석

  • MCP 프로토콜 준수

Related MCP server: Sequential Thinking MCP Server

설치

지엑스피1

구성

Claude Desktop 구성에 추가:

{
  "mcpServers": {
    "mcp-reasoner": {
      "command": "node",
      "args": ["path/to/mcp-reasoner/dist/index.js"],
    }
  }
}

검색 전략

빔 검색

  • 가장 유망한 경로의 고정 너비 세트를 유지합니다.

  • 단계별 추론에 최적

  • 가장 적합한 대상: 수학 문제, 논리 퍼즐

몬테카를로 트리 검색

  • 의사결정 공간의 시뮬레이션 기반 탐색

  • 탐사와 개발의 균형을 이룹니다

  • 가장 적합한 대상: 결과가 불확실한 복잡한 문제

참고: 몬테카를로 트리 탐색을 통해 클로드는 Arc AGI 벤치마크에서 매우 좋은 성적을 거두었습니다(공개 테스트에서 10점 만점에 6점). 반면 빔 탐색은 같은 퍼즐에서 10점 만점에 3점을 받았습니다. 매우 복잡한 작업의 경우, 클로드에게 빔 탐색 대신 MCTS 전략을 사용하도록 지시하는 것이 좋습니다.

알고리즘 세부 정보

  1. 검색 전략 선택

    • 빔 검색: 여러 솔루션 경로를 평가하고 순위를 매깁니다.

    • MCTS: 노드 선택 및 무작위 롤아웃을 위해 UCT를 사용합니다.

  2. 생각 점수 기준:

    • 세부 수준

    • 수학적 표현

    • 논리적 커넥터

    • 부모-자녀 관계의 힘

  3. 프로세스 관리

    • 트리 기반 상태 추적

    • 추론의 통계적 분석

    • 진행 상황 모니터링

사용 사례

  • 수학 문제

  • 논리 퍼즐

  • 단계별 분석

  • 복잡한 문제 분해

  • 의사결정 트리 탐색

  • 전략 최적화

향후 구현

  • 새로운 알고리즘 구현

    • 반복적 심화 깊이 우선 탐색(IDDFS)

    • 알파-베타 가지치기

특허

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.

Available Tools

1 tool
mcp-reasonerC

Advanced reasoning tool with multiple strategies including Beam Search and Monte Carlo Tree Search

ParametersJSON Schema
NameRequiredDescriptionDefault
nextThoughtNeededYesWhether another step is needed
strategyTypeNoReasoning strategy to use (beam_search or mcts)
thoughtYesCurrent reasoning step
thoughtNumberYesCurrent step number
totalThoughtsYesTotal expected steps

TDQS

C2.6/5.0
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. 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.

Conciseness4/5

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.

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 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.

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 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.

Purpose3/5

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.

Usage Guidelines2/5

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

C2.8/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness1/5

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

ActivityInactive
ResponsivenessNo issues

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