MCTS MCP Server

by angrysky56

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Server Quality Checklist

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Latest release: v1.0.0
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.
Average 2.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

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.
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. 'Run MCTS search iterations' implies a computational process but doesn't specify if it's read-only, destructive, or has side effects (e.g., modifying state). It also omits details like execution time, resource usage, or output format, leaving 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's front-loaded but overly terse, lacking necessary context that could justify more detail. While concise, it risks under-specification by omitting key information.
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 (a non-trivial algorithm) and the absence of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., search results, statistics) or how it integrates with other tools like 'initialize_mcts'. For a tool with computational behavior and no structured output, 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for both parameters ('iterations' and 'simulations_per_iteration'), including defaults and ranges. The description adds no additional meaning beyond the schema, such as explaining how these parameters affect search quality or performance. This meets the baseline of 3 since 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 'Run MCTS search iterations' specifies the verb ('Run') and resource ('MCTS search iterations'), providing a basic purpose. However, it's vague about what MCTS is (Monte Carlo Tree Search) and what domain it operates in (e.g., game AI, planning). It doesn't distinguish from siblings like 'initialize_mcts' or 'get_synthesis', leaving ambiguity in scope.
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 offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing initialization first), exclusions, or relationships with siblings like 'initialize_mcts' (which might be required before running) or 'get_synthesis' (which might retrieve results). This lack of context makes it hard for an agent to decide appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
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.
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.
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.

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