MCP Reasoner
MCP 推理器
针对 Claude Desktop 的系统推理 MCP 服务器实现,具有 Beam Search 和 Monte Carlo Tree Search (MCTS) 功能。
特征
双重搜索策略:
可配置宽度的束搜索
适用于复杂决策空间的 MCTS
思想评分与评估
基于树的推理路径
推理过程的统计分析
MCP 协议合规性
Related MCP server: Sequential Thinking MCP Server
安装
git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run build配置
添加到 Claude 桌面配置:
{
"mcpServers": {
"mcp-reasoner": {
"command": "node",
"args": ["path/to/mcp-reasoner/dist/index.js"],
}
}
}搜索策略
定向搜索
维护一组固定宽度的最有希望的路径
最适合逐步推理
最适合:数学问题、逻辑谜题
蒙特卡洛树搜索
基于模拟的决策空间探索
平衡探索与开发
最适合:结果不确定的复杂问题
**注意:**蒙特卡洛树搜索 (MCTS) 让 Claude 在 Arc AGI 基准测试中表现非常出色(公开测试得分 6/10),而定向搜索 (beam search) 在相同谜题中得分仅为 3/10。对于极其复杂的任务,建议 Claude 使用 MCTS 策略,而非定向搜索。
算法细节
搜索策略选择
定向搜索:评估并排序多个解决方案路径
MCTS:使用 UCT 进行节点选择和随机部署
思考评分依据:
细节级别
数学表达式
逻辑连接器
亲子关系强度
流程管理
基于树的状态跟踪
推理的统计分析
进度监控
用例
数学问题
逻辑谜题
逐步分析
复杂问题分解
决策树探索
策略优化
未来实施
实现新算法
迭代加深深度优先搜索(IDDFS)
Alpha-Beta 剪枝
执照
该项目根据 MIT 许可证获得许可 - 有关详细信息,请参阅LICENSE文件。
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
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