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run_mcts_search

Execute Monte Carlo Tree Search iterations to explore and analyze topics or text inputs, with customizable parameters for iterations and simulations per iteration.

Instructions

Run MCTS search iterations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
iterationsNoNumber of search iterations (1-10)
simulations_per_iterationNoSimulations per iteration (1-20)
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.

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