MCP Reasoner
Razonador MCP
Una implementación de servidor MCP de razonamiento sistemático para Claude Desktop con capacidades de búsqueda de haz y búsqueda de árbol de Monte Carlo (MCTS).
Características
Estrategias de búsqueda dual:
Búsqueda de haz con ancho configurable
MCTS para espacios de decisión complejos
Puntuación y evaluación del pensamiento
Caminos de razonamiento basados en árboles
Análisis estadístico del proceso de razonamiento
Cumplimiento del protocolo MCP
Related MCP server: Sequential Thinking MCP Server
Instalación
git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run buildConfiguración
Agregar a la configuración de Claude Desktop:
{
"mcpServers": {
"mcp-reasoner": {
"command": "node",
"args": ["path/to/mcp-reasoner/dist/index.js"],
}
}
}Estrategias de búsqueda
Búsqueda de haz
Mantiene un conjunto de ancho fijo de rutas más prometedoras
Óptimo para el razonamiento paso a paso
Ideal para: problemas matemáticos, rompecabezas lógicos.
Búsqueda de árboles de Monte Carlo
Exploración del espacio de decisión basada en simulación
Equilibra la exploración y la explotación
Ideal para: Problemas complejos con resultados inciertos
Nota: La búsqueda de árbol de Monte Carlo le permitió a Claude obtener un excelente rendimiento en el benchmark Arc AGI (puntuación de 6/10 en la prueba pública), mientras que la búsqueda de haz obtuvo un 3/10 en los mismos rompecabezas. Para tareas muy complejas, conviene indicarle a Claude que utilice la estrategia MCTS en lugar de la búsqueda de haz.
Detalles del algoritmo
Selección de la estrategia de búsqueda
Beam Search: evalúa y clasifica múltiples rutas de solución
MCTS: utiliza UCT para la selección de nodos y lanzamientos aleatorios
Puntuación del pensamiento basada en:
Nivel de detalle
Expresiones matemáticas
Conectores lógicos
Fortaleza de la relación padre-hijo
Gestión de procesos
Seguimiento del estado basado en árboles
Análisis estadístico del razonamiento
Seguimiento del progreso
Casos de uso
Problemas matemáticos
Rompecabezas lógicos
Análisis paso a paso
Descomposición de problemas complejos
Exploración del árbol de decisiones
Optimización de la estrategia
Implementaciones futuras
Implementar nuevos algoritmos
Búsqueda iterativa de profundidad (IDDFS)
Poda Alfa-Beta
Licencia
Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.
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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