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parmarjh

MCP Reasoner

by parmarjh
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TypeScript

MCP Reasoner

A systematic reasoning MCP server implementation for Claude Desktop featuring both Beam Search and Monte Carlo Tree Search (MCTS) capabilities.

Features

  • Dual search strategies:

    • Beam search with configurable width

    • MCTS for complex decision spaces

  • Thought scoring and evaluation

  • Tree-based reasoning paths

  • Statistical analysis of reasoning process

  • MCP protocol compliance

Related MCP server: Sequential Thinking MCP Server

Installation

git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run build

Configuration

Add to Claude Desktop config:

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

Search Strategies

Beam Search

  • Maintains fixed-width set of most promising paths

  • Optimal for step-by-step reasoning

  • Best for: Mathematical problems, logical puzzles

Monte Carlo Tree Search

  • Simulation-based exploration of decision space

  • Balances exploration and exploitation

  • Best for: Complex problems with uncertain outcomes

Note: Monte Carlo Tree Search allowed Claude to perform really well on the Arc AGI benchmark (scored 6/10 on the public test), whereas beam search yielded a (3/10) on the same puzzles. For super complex tasks, you'd want to direct Claude to utilize the MCTS strategy over the beam search.

Algorithm Details

  1. Search Strategy Selection

    • Beam Search: Evaluates and ranks multiple solution paths

    • MCTS: Uses UCT for node selection and random rollouts

  2. Thought Scoring Based On:

    • Detail level

    • Mathematical expressions

    • Logical connectors

    • Parent-child relationship strength

  3. Process Management

    • Tree-based state tracking

    • Statistical analysis of reasoning

    • Progress monitoring

Use Cases

  • Mathematical problems

  • Logical puzzles

  • Step-by-step analysis

  • Complex problem decomposition

  • Decision tree exploration

  • Strategy optimization

Future Implementations

  • Implement New Algorithms

    • Iterative Deepening Depth-First Search (IDDFS)

    • Alpha-Beta Pruning

License

This project is licensed under the MIT License - see the LICENSE file for details.

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.

Install Server
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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

Resources

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Related MCP Connectors

Related MCP Servers

  • Sequential Thinking MCP Server

    LangGPT
    A
    license
    A
    quality
    D
    maintenance
    An MCP server that enables Claude to break down complex problems into manageable steps with support for revision and branching, facilitating dynamic and reflective problem-solving through a structured thinking process.
    1
    17
    MIT

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