stochastic_algorithm

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

No annotations exist, so the description must fully disclose behavior. It only mentions the algorithms are probabilistic, but does not explain side effects, determinism, performance, or any constraints. Critical behavioral details are missing.

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 sentence, making it concise. However, it is a noun phrase fragment rather than a full imperative sentence, which slightly reduces clarity. Still, it is front-loaded with the core verb 'Probabilistic algorithms'.

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 tool has 4 parameters, no output schema, and no annotations, the description is too brief. It does not explain how to construct the problem string, list algorithm options, or describe return values. Comprehensive usage examples or constraints are absent.

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?

The input schema has 100% description coverage, providing clear definitions for each parameter. The description adds value by giving specific examples for the 'parameters' object (e.g., MCTS, Simulated Annealing), which helps the agent understand valid entries beyond the schema's generic 'additionalProperties'.

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 clearly states the tool applies probabilistic algorithms (MDPs, MCTS, Bayesian optimization) for decision-making under uncertainty. This is specific and distinguishes it from sibling reasoning tools, which focus on structured thinking rather than algorithmic execution.

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?

The description implies usage when dealing with decision-making under uncertainty, but it does not explicitly state when to use this tool versus alternatives like collaborative_reasoning or scientific_method. No when-not or exclusion criteria are provided.

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