autotune_analyze

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states it 'analyzes' and 'gets parameters,' but does not disclose whether the operation is read-only, requires network calls, involves side effects, or has any auth/rate-limit implications. This is a significant gap for an unannotated tool.

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, concise sentence that front-loads the core action ('Analyze a message'). There is no filler or redundant content, meeting the standard of concise and efficient writing.

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?

The tool has 3 parameters, an output schema, and no annotations. The description is too minimal to provide a complete picture—it lacks details on what 'optimal' means, how the analysis works, expected outputs, or any constraints. Given the complexity, this is under-specified.

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 schema description coverage is 0%. The description only mentions 'message' indirectly ('Analyze a message'), but provides no explanation of the 'strategy' or 'conversation_history' parameters. Since the description does not compensate for the lack of schema documentation, parameter semantics are poorly conveyed.

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's function: 'Analyze a message and get optimal LLM sampling parameters from AutoTune.' This is a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools (e.g., single_chat, list_models), though the unique 'AutoTune' reference helps.

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?

No usage context is provided. The description does not indicate when to use this tool versus alternatives, nor does it mention any prerequisite conditions or exclusions. It simply states what the tool does without guidance on selection.

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