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MCTS MCP Server

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MCTS MCP 服务器

模型上下文协议 (MCP) 服务器公开了用于 AI 辅助分析和推理的高级贝叶斯蒙特卡洛树搜索 (MCTS) 引擎。

概述

此 MCP 服务器使 Claude 能够使用蒙特卡洛树搜索 (MCTS) 算法对主题、问题或文本输入进行深入的探索性分析。MCTS 算法采用贝叶斯方法系统地探索不同的角度和解释，并通过多次迭代得出富有洞察力的分析结果。

Related MCP server: MCP Advanced Reasoning Server

特征

  • 贝叶斯 MCTS ：使用概率方法在分析过程中平衡探索与利用

  • 多次迭代分析：支持多次迭代思考，每次迭代进行多次模拟

  • 状态持久性：记住同一聊天中的关键结果、不合适的方法和回合之间的先验

  • 方法分类法：将产生的思想分为不同的哲学方法和家族

  • 汤普森抽样：可以使用汤普森抽样或UCT进行节点选择

  • 意外检测：识别令人惊讶或新颖的分析方向

  • 意图分类：了解用户何时想要开始新的分析或继续之前的分析

用法

服务器以可复制粘贴的格式向您的 LLM 公开了许多工具，详见下文，以供系统提示。

当你要求 Claude 对某个主题或问题进行深入分析时，它将自动利用这些工具，使用 MCTS 算法和分析工具探索不同的角度。

替代文本

工作原理

MCTS MCP 服务器使用本地推理方法，而不是尝试直接调用 LLM。这与 MCP 协议兼容，该协议旨在供 AI 助手（如 Claude）调用工具，而不是供工具自行调用 AI 模型。

当 Claude 要求服务器执行分析时，服务器：

  1. 使用问题初始化 MCTS 系统

  2. 使用 MCTS 算法运行多次探索迭代

  3. 为各种分析任务生成确定性响应

  4. 返回搜索过程中找到的最佳分析

安装

克隆存储库：

该设置使用 UV（Astral UV），它是 pip 的更快替代方案，可提供改进的依赖关系解析。

  1. 确保已安装 Python 3.10+

  2. 运行安装脚本：

./setup.sh

这将：

  • 如果尚未安装，请安装 UV

  • 使用 UV 创建虚拟环境

  • 使用 UV 安装所需的软件包

  • 创建必要的状态目录

或者，您可以手动设置：

# Install UV if not already installed
curl -fsSL https://astral.sh/uv/install.sh | bash
# Create and activate a virtual environment
uv venv .venv
source .venv/bin/activate

# Install dependencies
uv pip install -r requirements.txt

Claude 桌面集成

与 Claude Desktop 集成：

  1. 从此存储库复制claude_desktop_config.json的内容

  2. 将其添加到您的 Claude Desktop 配置中（通常位于~/.claude/claude_desktop_config.json ）

  3. 如果配置文件尚不存在，请创建它并添加该项目的claude_desktop_config.json中的内容

  4. 重启Claude桌面

示例配置：

{
  "mcpServers": {
    "MCTSServer": {
      "command": "uv",
      "args": [
        "run",
        "--directory", "/home/ty/Repositories/ai_workspace/mcts-mcp-server/src/mcts_mcp_server",
        "server.py"
      ],
      "env": {
        "PYTHONPATH": "/home/ty/Repositories/ai_workspace/mcts-mcp-server"
      }
    }
  }
}

确保更新路径以匹配系统上 MCTS MCP 服务器的位置。

建议的系统提示和更新工具（包括 Ollama 集成），即将以下块放置在项目说明中：

MCTS server and usage instructions:

MCTS server and usage instructions:
list_ollama_models()  # Check what models are available
set_ollama_model("cogito:latest")  # Set the model you want to use
initialize_mcts(question="Your question here", chat_id="unique_id")  # Initialize analysis
run_mcts(iterations=1, simulations_per_iteration=5)  # Run the analysis

After run_mcts is called it can take wuite a long time ie minutes to hours
- so you may discuss any ideas or questions or await user confirmation of the process finishing,
- then proceed to synthesis and analysis tools on resumption of chat.

## MCTS-MCP Tools Overview

### Core MCTS Tools:
- `initialize_mcts`: Start a new MCTS analysis with a specific question
- `run_mcts`: Run the MCTS algorithm for a set number of iterations/simulations
- `generate_synthesis`: Generate a final summary of the MCTS results
- `get_config`: View current MCTS configuration parameters
- `update_config`: Update MCTS configuration parameters
- `get_mcts_status`: Check the current status of the MCTS system

Default configuration prioritizes speed and exploration, but you can customize parameters like exploration_weight, beta_prior_alpha/beta, surprise_threshold.

## Configuration

You can customize the MCTS parameters in the config dictionary or through Claude's `update_config` tool. Key parameters include:

- `max_iterations`: Number of MCTS iterations to run
- `simulations_per_iteration`: Number of simulations per iteration
- `exploration_weight`: Controls exploration vs. exploitation balance (in UCT)
- `early_stopping`: Whether to stop early if a high-quality solution is found
- `use_bayesian_evaluation`: Whether to use Bayesian evaluation for node scores
- `use_thompson_sampling`: Whether to use Thompson sampling for selection

Articulating Specific Pathways:
Delving into the best_path nodes (using mcts_instance.get_best_path_nodes() if you have the instance) and examining the sequence of thought and content
at each step can provide a fascinating micro-narrative of how the core insight evolved.

Visualizing the tree (even a simplified version based on export_tree_summary) could also be illuminating and I will try to set up this feature.

Modifying Parameters: This is a great way to test the robustness of the finding or explore different "cognitive biases" of the system.

Increasing Exploration Weight: Might lead to more diverse, less obviously connected ideas.

Decreasing Exploration Weight: Might lead to deeper refinement of the initial dominant pathways.

Changing Priors (if Bayesian): You could bias the system towards certain approaches (e.g., increase alpha for 'pragmatic') to see how it influences the
outcome.

More Iterations/Simulations: Would allow for potentially deeper convergence or exploration of more niche pathways.

### Ollama Integration Tools:
- `list_ollama_models`: Show all available local Ollama models
- `set_ollama_model`: Select which Ollama model to use for MCTS
- `run_model_comparison`: Run the same MCTS process across multiple models

### Results Collection:
- Automatically stores results in `/home/ty/Repositories/ai_workspace/mcts-mcp-server/results`
- Organizes by model name and run ID
- Stores metrics, progress info, and final outputs

# MCTS Analysis Tools

This extension adds powerful analysis tools to the MCTS-MCP Server, making it easy to extract insights and understand results from your MCTS runs.

The MCTS Analysis Tools provide a suite of integrated functions to:

1. List and browse MCTS runs
2. Extract key concepts, arguments, and conclusions
3. Generate comprehensive reports
4. Compare results across different runs
5. Suggest improvements for better performance

## Available Run Analysis Tools

### Browsing and Basic Information

- `list_mcts_runs(count=10, model=None)`: List recent MCTS runs with key metadata
- `get_mcts_run_details(run_id)`: Get detailed information about a specific run
- `get_mcts_solution(run_id)`: Get the best solution from a run

### Analysis and Insights

- `analyze_mcts_run(run_id)`: Perform a comprehensive analysis of a run
- `get_mcts_insights(run_id, max_insights=5)`: Extract key insights from a run
- `extract_mcts_conclusions(run_id)`: Extract conclusions from a run
- `suggest_mcts_improvements(run_id)`: Get suggestions for improvement

### Reporting and Comparison

- `get_mcts_report(run_id, format='markdown')`: Generate a comprehensive report (formats: 'markdown', 'text', 'html')
- `get_best_mcts_runs(count=5, min_score=7.0)`: Get the best runs based on score
- `compare_mcts_runs(run_ids)`: Compare multiple runs to identify similarities and differences

## Usage Examples

# To list your recent MCTS runs:

list_mcts_runs()

# To get details about a specific run:

get_mcts_run_details('cogito:latest_1745979984')

### Extracting Insights

# To get key insights from a run:

get_mcts_insights(run_id='cogito:latest_1745979984')

### Generating Reports

# To generate a comprehensive markdown report:

get_mcts_report(run_id='cogito:latest_1745979984', format='markdown')


### Improving Results

# To get suggestions for improving a run:

suggest_mcts_improvements(run_id='cogito:latest_1745979984')

### Comparing Runs

To compare multiple runs:

compare_mcts_runs(['cogito:latest_1745979984', 'qwen3:0.6b_1745979584'])

## Understanding the Results

The analysis tools extract several key elements from MCTS runs:

1. **Key Concepts**: The core ideas and frameworks in the analysis
2. **Arguments For/Against**: The primary arguments on both sides of a question
3. **Conclusions**: The synthesized conclusions or insights from the analysis
4. **Tags**: Automatically generated topic tags from the content

## Troubleshooting

If you encounter any issues with the analysis tools:

1. Check that your MCTS run completed successfully (status: "completed")
2. Verify that the run ID you're using exists and is correct
3. Try listing all runs to see what's available: `list_mcts_runs()`
4. Make sure the `.best_solution.txt` file exists in the run's directory

## Advanced Example Usage

### Customizing Reports

You can generate reports in different formats:

# Generate a markdown report

report = get_mcts_report(run_id='cogito:latest_1745979984', format='markdown')

# Generate a text report

report = get_mcts_report(run_id='cogito:latest_1745979984', format='text')

# Generate an HTML report

report = get_mcts_report(run_id='cogito:latest_1745979984', format='html')

### Finding the Best Runs

To find your best-performing runs:

best_runs = get_best_mcts_runs(count=3, min_score=8.0)

This returns the top 3 runs with a score of at least 8.0.

## Simple Usage Instructions

1. **Changing Models**:

   list_ollama_models()  # See available models
   set_ollama_model("qwen3:0.6b")  # Set to fast small model

2. **Starting a New Analysis**:

   initialize_mcts(question="Your question here", chat_id="unique_identifier")

3. **Running the Analysis**:

   run_mcts(iterations=3, simulations_per_iteration=10)

4. **Comparing Performance**:

   run_model_comparison(question="Your question", iterations=2)

5. **Getting Results**:

   generate_synthesis()  # Final summary of results
   get_mcts_status()     # Current status and metrics

示例提示

  • “分析人工智能对人类创造力的影响”

  • “继续探索这个话题的伦理层面”

  • “您在上次运行中发现的最佳分析是什么？”

  • “这个 MCTS 流程是如何运作的？”

  • “显示当前的 MCTS 配置”

替代文本

对于开发人员

# Activate virtual environment
source .venv/bin/activate

# Run the server directly (for testing)
uv run server.py

# OR use the MCP CLI tools
uv run -m mcp dev server.py

测试服务器

测试服务器是否正常工作：

# Activate the virtual environment
source .venv/bin/activate

# Run the test script
python test_server.py

这将测试 LLM 适配器以确保其正常工作。

贡献

欢迎为改进 MCTS MCP 服务器做出贡献。以下是一些潜在的改进领域：

  • 改进本地推理适配器以实现更复杂的分析

  • 添加更复杂的思维模式和评估策略

  • 增强树可视化和结果报告

  • 优化MCTS算法参数

许可证： MIT

Available Tools

6 tools
get_statusC

Get the current MCTS status

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.9/5.0
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 of behavioral disclosure. 'Get the current MCTS status' implies a read-only operation, but it doesn't specify whether this requires authentication, has rate limits, returns real-time or cached data, or what format the status information takes. For a tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence: 'Get the current MCTS status'. It's front-loaded with the core action and resource, with no redundant words or unnecessary elaboration. This is an excellent example of conciseness for a simple tool.

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 (a status-checking tool with no output schema and no annotations), the description is incomplete. It doesn't explain what 'MCTS status' includes (e.g., operational state, error messages, performance metrics) or how the result should be interpreted. Without annotations or output schema, the description should provide more context about the return value and usage scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, which is appropriate since there are none to explain. This meets the baseline of 4 for zero-parameter tools, as there's no need to compensate for missing schema information.

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 'Get the current MCTS status' clearly states the action (Get) and resource (MCTS status), making the purpose understandable. However, it doesn't differentiate this tool from its siblings like 'get_synthesis' or 'initialize_mcts', leaving ambiguity about what specifically distinguishes 'status' from other retrieval operations. The description avoids tautology but lacks specificity about what 'MCTS status' entails.

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?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_synthesis', 'list_available_models', and 'run_mcts_search', there's no indication of whether this tool is for checking system health, monitoring progress, or retrieving configuration states. The absence of usage context leaves the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_synthesisC

Generate a final synthesis of the MCTS results

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.8/5.0
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 of behavioral disclosure. It states the tool generates a synthesis, implying a read operation, but doesn't clarify if this is a computation-heavy process, if it requires specific states (e.g., completed MCTS runs), or what the output entails (e.g., summary vs. detailed analysis). The description lacks details on performance, side effects, or error conditions.

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, clear sentence that directly states the tool's function without redundancy. It's appropriately sized for a zero-parameter tool, though it could be slightly more informative (e.g., adding context about when to use it) without sacrificing conciseness.

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 MCTS operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the synthesis includes (e.g., statistics, recommendations), how it relates to sibling tools, or any behavioral traits. For a tool that likely processes search results, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics beyond what the schema provides, earning a baseline score of 4 for this dimension, as it appropriately avoids unnecessary details.

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 the tool's purpose ('Generate a final synthesis of the MCTS results') with a clear verb ('Generate') and resource ('MCTS results'), but it doesn't distinguish this from sibling tools like 'run_mcts_search' or 'get_status'. The purpose is understandable but lacks specificity about what makes this synthesis different from other operations.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether MCTS must be initialized or run first), timing (e.g., after search completion), or how it differs from siblings like 'get_status' or 'run_mcts_search'. Without such context, usage is implied but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

initialize_mctsC

Initialize MCTS for a question

ParametersJSON Schema
NameRequiredDescriptionDefault
chat_idNoUnique identifier for this conversationdefault
modelNoModel name (optional)
providerNoLLM providergemini
questionYesThe question to analyze

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 of behavioral disclosure. It states 'Initialize MCTS' but doesn't explain what this does operationally (e.g., sets up state, allocates resources, requires specific permissions), what happens on failure, or any side effects. This leaves significant gaps in understanding the tool's behavior.

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 with no wasted words, making it appropriately sized. However, it lacks front-loading of critical details (e.g., purpose or key parameters), which slightly reduces its effectiveness despite its brevity.

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 initializing a system like MCTS with 4 parameters and no annotations or output schema, the description is incomplete. It fails to explain what initialization entails, what state is created, or how it interacts with sibling tools, leaving the agent with insufficient context for effective use.

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 fully documents all 4 parameters. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify the relationship between parameters like 'chat_id' and 'question'), resulting in a baseline score of 3 as the schema does the heavy lifting.

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 the tool 'Initialize MCTS for a question' which provides a basic verb ('Initialize') and resource ('MCTS'), but it's vague about what MCTS is and what initialization entails. It doesn't distinguish from siblings like 'run_mcts_search' or 'get_synthesis', leaving the specific purpose unclear beyond a general setup action.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing (e.g., before running search), or exclusions, leaving the agent to infer usage from context without explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_available_modelsC

List available models for a provider

ParametersJSON Schema
NameRequiredDescriptionDefault
providerNoProvider namegemini

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, if it requires authentication, what format the list returns, or any rate limits or side effects. The description adds no behavioral context beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple listing tool with one parameter.

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?

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'available models' means (e.g., model names, capabilities, statuses), how results are structured, or any dependencies on other tools like 'set_provider'. The agent lacks critical context to use this effectively.

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?

The schema has 100% description coverage, with the single parameter 'provider' documented as 'Provider name' with a default value. The description adds no additional meaning about parameters beyond implying the tool filters by provider, which is already clear from the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('available models for a provider'), making the purpose immediately understandable. It doesn't distinguish from sibling tools like 'get_status' or 'set_provider', but the verb+resource combination is specific enough for basic understanding.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, appropriate contexts, or relationships to sibling tools like 'set_provider' (which might affect available models) or 'get_synthesis' (which might use these models).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

set_providerC

Set the LLM provider and model

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoModel name (optional)
providerNoProvider namegemini

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool performs a 'Set' action which implies mutation/write behavior, but doesn't disclose whether this requires specific permissions, whether changes are persistent, what happens to existing configurations, or any side effects. No information about error conditions, rate limits, or authentication requirements is provided.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core action and target. Every word earns its place by conveying essential information about what the tool does.

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?

For a configuration/mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after setting the provider/model, whether the change is immediate or requires restart, what values are valid, or what the tool returns. The agent lacks crucial information about this write operation's behavior and consequences.

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 both parameters fully with their types, optionality, and default values. The description adds minimal value beyond what's in the schema - it mentions 'LLM provider and model' which maps to the parameters but doesn't provide additional context about valid values, constraints, or relationships between parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Set') and identifies the target resources ('LLM provider and model'). It distinguishes this as a configuration tool rather than a query or execution tool. However, it doesn't explicitly differentiate from potential sibling tools that might also configure aspects of the system, though none of the listed siblings appear to be direct alternatives.

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?

The description provides no guidance on when to use this tool versus alternatives or when not to use it. There's no mention of prerequisites, timing considerations, or relationship to other tools like 'initialize_mcts' or 'list_available_models' that might be related to system setup. The agent must infer usage context entirely from the tool name and parameters.

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. 6 tool updatesv1.0.0
    • First observedget_status
    • First observedget_synthesis
    • First observedinitialize_mcts
    • First observedlist_available_models
    • First observedrun_mcts_search
    • First observedset_provider
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TDQS

B3.3/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no ambiguity: get_status retrieves current state, get_synthesis generates final output, initialize_mcts sets up the process, list_available_models shows options, run_mcts_search performs iterations, and set_provider configures the model. The tools cover different stages and aspects of the MCTS workflow without overlap.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case throughout: get_status, get_synthesis, initialize_mcts, list_available_models, run_mcts_search, set_provider. The naming is predictable and readable, using clear verbs like 'get', 'initialize', 'list', 'run', and 'set' that accurately describe each action.

Tool Count5/5

With 6 tools, the server is well-scoped for managing MCTS processes, covering initialization, configuration, execution, and result retrieval. Each tool earns its place in the workflow, providing a focused set without being too sparse or bloated, which is typical for a specialized domain like MCTS.

Completeness4/5

The tool surface covers the core MCTS lifecycle comprehensively: initialization (initialize_mcts), configuration (set_provider, list_available_models), execution (run_mcts_search), and results (get_status, get_synthesis). A minor gap exists in lacking tools for intermediate monitoring or adjusting parameters during search, but agents can work around this with the provided tools.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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