list_llm_models

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

No annotations are provided, so the description carries the full burden. It usefully discloses the trace-scanning behavior via the limit parameter ('Maximum traces to analyze for model discovery') and describes the return shape (count, request_count, first_seen, last_seen), but says nothing about permissions, read-only status, or whether the trace scan is expensive/rate-limited.

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?

Front-loads the purpose before the Args/Returns blocks and uses clean sections. The second sentence ('Discovers what models are deployed and tracks their usage patterns') largely restates the first, a minor redundancy, but overall the text is well-sized and earns its space.

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?

An output schema exists so return values need not be re-explained, yet the description still summarizes them concisely. All five params are documented despite 0% schema coverage. The only real gap is behavioral context (no annotations, no permission or sibling-routing guidance), which keeps it short of a 5.

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 coverage is 0%, so the description must compensate, and it does: each of the five parameters gets a meaning, ISO 8601 format guidance with an example, a provider example list (openai, anthropic, cohere), and the limit default. Format and filtering semantics are clear even though it does not explain null/default behavior for the optional filters.

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?

States a specific verb and resource (list LLM models) plus scope ("being used with usage statistics") and adds the intent "discovers what models are deployed and tracks their usage patterns." However, it never distinguishes itself from the near-identical sibling get_llm_model_stats, so an agent cannot route between them from the description alone.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternative tools. With siblings like get_llm_model_stats and get_llm_usage in the same family, the absence of any disambiguation leaves selection to guesswork; only the implicit 'discovery' framing hints at context.

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