llm_model_export

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

With no annotations, the description must fully disclose behavior. It states the tool exports data to a file and returns a path and record count, but does not mention side effects, required permissions, or whether it is read-only. This is adequate but not thorough.

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 concise with two sentences for purpose and usage, plus structured Args/Returns sections. Every part serves a clear function, and the important information is front-loaded.

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?

For a simple export tool with one optional parameter and an output schema, the description covers purpose, usage, parameters, and return value. Nothing obvious is missing.

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 only shows 'format' as a string with default 'csv'. The description adds the enum values 'csv' and 'json' and explains the parameter's purpose (export format). This adds meaningful information beyond the schema.

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 verb 'Export' and the resource 'model tracking data' (complete routing history). It is distinct from sibling tools (e.g., llm_analyze, llm_query) as it focuses on exporting for external analysis. No ambiguity.

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 provides clear context that the tool is for exporting routing history to files for analysis in external tools like Excel, Python, R. It implies when to use but does not explicitly state when not to use or mention alternatives, though no direct alternative exists among siblings.

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