llm_model_export

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

No annotations are provided, and the description does not disclose behavioral traits such as side effects, permissions, or file overwrite behavior. It mentions exporting and returning a path/count, but a read-only claim is not explicit.

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 short and well-structured: purpose statement, usage context, then organized Args and Returns. Every sentence adds value without redundancy.

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 one optional parameter and presence of an output schema, the description covers the main functionality and return value. However, it lacks context about prerequisites (e.g., needing a model session) or what 'routing history' entails. Still adequate for the tool's simplicity.

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?

Schema has no property descriptions (0% coverage), so the description adds meaning by listing format options (csv, json) and default. This compensates for the lack of schema detail, though it could be more precise about allowed values.

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?

Clearly states it exports model tracking data (routing history) to a file for external analysis. The verb 'export' and resource are specific, but it does not differentiate from sibling tools like llm_model_eval or llm_model_usage.

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?

Provides examples of when to use (for analysis in spreadsheets, Python, R), but does not explicitly state when not to use it or suggest alternatives. Usage context is implied but lacks exclusions.

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