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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely useful non-annotation context: the catalog is a fixed set of "30+ models" from eight named providers, which tells the agent the coverage is bounded and a lookup for an unlisted model may fail. It still doesn't say what happens on an unknown model name (error vs empty result).

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?

Two sentences: the first front-loads the action and enumerates the return fields, the second states catalog breadth and providers. No filler, no restatement of the tool name, and the most decision-relevant information (what you get back) comes first.

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?

With an output schema present and full annotation coverage, the description need not explain return values, and it is complete enough to call correctly: one param, clear examples, known catalog scope. The one remaining gap is unknown-model behavior, which an agent would want to know before calling with a speculative name.

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?

One required parameter with 100% schema description coverage, including concrete examples ("gpt-4o", "claude-3.5-sonnet"), so the schema already carries the semantics. The description adds nothing about accepted naming variants, aliases, or case sensitivity beyond the schema, which is the baseline-3 outcome when the schema does the heavy lifting.

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?

Specific verb plus resource ("Get detailed specs for an AI model") immediately followed by the exact fields returned: context window, pricing, knowledge cutoff, provider, multimodal, reasoning, features. This clearly separates it from list/compare-style siblings in spirit, but no sibling (e.g. list_llm_models, compare_models, context_window_check) is named, so the differentiation is implicit rather than explicit.

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?

Usage is only implied by the purpose: pass a model name to retrieve its specs. There is no statement of when to prefer this over list_llm_models (to discover valid names) or compare_models (side-by-side), and no note about model-name format beyond the schema examples. For a single-param lookup tool this is workable but leaves routing ambiguous in a 40-sibling cluster.

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