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

Although no annotations are provided, the description thoroughly discloses behavior: it differentiates active vs. inactive models, explains that absent period figures are missing data 'NOT zero sales', states that results appear in unavailableFields, and warns that spelling is forgiving and the response indicates how it matched. It even instructs the agent to verify the name field before quoting numbers. This is exceptional transparency for a tool with zero annotations.

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?

Every sentence earns its place: purpose and scope first, then routing guidance, then return behavior for both active and inactive models, then matching/verification caveats. It is long but information-dense with no repetition or filler. The critical edge-case semantics are clearly front-loaded near the relevant behavior.

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 no output schema and no annotations, the description carries the full burden and succeeds. It explains what figures are returned for active models, what an inactive model returns, how missing data is represented, and how matching works. It gives an agent enough to select, invoke, and interpret the tool's response correctly, including a caution to verify the matched 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?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful value beyond the schema by explaining query semantics: examples of valid queries, forgiving spelling normalization ('ID.4 = ID 4 = id4'), and the need to verify the returned name. It does not elaborate on kind, limit, or grouped, but the schema already covers those fully.

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: 'Look up Danish registration numbers for a SPECIFIC car model by name'. It gives concrete examples and explicit scope ('Covers every model since 2018'), and distinguishes itself from get_top_models and get_market_stats. An agent can immediately tell what this tool does and how it differs from siblings.

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?

Explicitly says when to use it: 'Use this when the question names a model or brand'. It also states alternatives and why they are not suitable: get_top_models covers only top sellers of one month and get_market_stats only top 20 brands. No ambiguity about routing.

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