mcp-reasoner
MCP 리즈너
Beam Search와 Monte Carlo Tree Search(MCTS)를 모두 사용할 수 있는 Claude Desktop용 추론 구현입니다. 솔직히 말해서 이것은 Claude가 복잡한 문제를 해결하는 데 더 능숙해질 수 있는지 시험해 보려는 방법으로 시작되었습니다... 결국 우리는 확실히 할 수 있다는 것이 밝혀졌습니다.
현재 버전:
v2.0.0
새로운 소식:
실험적 추론 알고리즘 2개가 추가되었습니다.
지엑스피1
mcts-001-alpha 와 mcts-001alt-alpha 는 어떻게 되었나요?
간단히 말해서, 쓸모가 없었고 기본
mcts방식과 거의 비슷했습니다. 초기 테스트 결과, 기본적인 사고 과정에서 나온 결과는 거의 비슷했으며, 단순히 정책 시뮬레이션을 추가하는 것만으로는 효과가 없을 수 있음을 보여주었습니다.
그렇다면 왜 지금 정책 시뮬레이션 레이어를 추가했을까요?
저는 정책과 검색을 함께 통합하는 것이 중요하다고 생각합니다. 대부분의 알고리즘이 이를 구현하는 방식이기 때문입니다.
이전 버전:
v1.1.0
검색 매개변수에 대한 모델 제어가 추가되었습니다.
beamWidth - Claude가 추적할 경로 수(1~10)를 조정할 수 있도록 합니다.
numSimulations - MCTS 시뮬레이션 횟수(1~150) 미세 조정
특징
다음 두 가지 검색 전략을 전환할 수 있습니다.
빔 검색(간단한 작업에 적합)
MCTS(사물이 복잡해질 때) 알파 변형(위 참조)
다양한 추론 경로가 얼마나 좋은지 추적합니다.
Claude가 문제를 생각하는 다양한 방법을 모두 매핑합니다.
추론 과정이 어떻게 진행되었는지 분석합니다.
MCP 프로토콜을 따릅니다(물론)
Related MCP server: MCP Advanced Reasoning Server
설치
git clone https://github.com/frgmt0/mcp-reasoner.git
OR clone the original:
git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run build구성
Claude Desktop 구성에 추가:
{
"mcpServers": {
"mcp-reasoner": {
"command": "node",
"args": ["path/to/mcp-reasoner/dist/index.js"],
}
}
}테스트
[추가 테스트가 곧 진행됩니다]
벤치마크
[벤치마킹은 곧 추가될 예정입니다.]
테스트할 주요 벤치마크:
수학500
GPQA-다이아몬드
GMSK8
아마도 다국어 사용자 및/또는 SWE-Bench 사용자일 것입니다.
특허
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
Available Tools
1 toolmcp-reasonerC
Advanced reasoning tool with multiple strategies including Beam Search and Monte Carlo Tree Search
| Name | Required | Description | Default |
|---|---|---|---|
| beamWidth | No | Number of top paths to maintain (n-sampling). Defaults to 3 if not specified | |
| nextThoughtNeeded | Yes | Whether another step is needed | |
| numSimulations | No | Number of MCTS simulations to run. Defaults to 50 if not specified | |
| 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 does not disclose behavioral traits such as rate limits, idempotency, or side effects. The description only mentions 'advanced reasoning' without explaining what operations are performed or their implications.
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 sentence, highly concise. It front-loads the core purpose. However, it may be too brief for the tool's complexity, but it is not verbose.
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 (7 parameters, no output schema, no annotations), the description is insufficient. It does not explain the reasoning process, how strategies differ, or how the parameters like beamWidth and numSimulations interact. More context is needed for effective use.
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?
The input schema has 100% coverage with descriptions for all 7 parameters. The description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate as the schema handles the semantics well.
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 clearly states the tool is for advanced reasoning with specific strategies (Beam Search, MCTS). It is specific about the resource and action, though not as precise as a verb+resource pairing. No siblings exist to differentiate from, so the description adequately distinguishes itself.
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?
The description provides no guidance on when to use this tool versus alternatives, no context on strategy selection, and no prerequisites or use cases. The sibling-tools list is empty, but the description should still offer usage suggestions.
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 tool update
v1.0.0- First observed
mcp-reasoner
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'mcp-reasoner' has a clearly distinct purpose focused on advanced reasoning strategies.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'mcp-reasoner' follows a clear, descriptive pattern that matches the server name.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. While it might be appropriate for a highly specialized niche, the description suggests advanced reasoning with multiple strategies, which could benefit from more granular tools (e.g., separate tools for different search strategies).
It is difficult to assess completeness with only one tool, as there is no clear domain surface to evaluate. The tool covers reasoning strategies, but without additional tools (e.g., for input/output, configuration, or specific reasoning modes), there are likely gaps in a full reasoning workflow.
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