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

With no annotations, the description carries the transparency burden and does more than repeat the name: it discloses the matching logic (activity code + geographic area), the output dimensions (size, age, status), and the fact that results are intended for ranking. It also mentions per-call pricing. It omits caveats like pagination or edge cases, but for a read-style lookup the core behavior is well disclosed.

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?

Three compact sentences: definition and outputs, target use cases, and pricing. The most important information is front-loaded and there is no filler or repetition of schema details.

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 2-parameter tool with no annotations and no output schema, the description covers purpose, algorithm, output fields, use cases, and cost. The main gap is operational detail: an agent still has to infer what values q and zone accept and how results are paginated or structured. It is adequate but not complete.

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 schema provides only bare names (q, zone) with 0% documentation coverage, so the description must compensate. It plausibly maps q to the French company and zone to the geographic area, but it never labels the parameters explicitly or states expected formats (e.g., SIREN, name, city, postal code). This is partial compensation, not complete.

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 opens with a specific verb and resource ('Identify the competitors of a French company') and immediately defines the semantics: businesses sharing the same activity code in the relevant geographic area. It also names the returned dimensions (size, age, current status), which distinguishes it from generic or sibling data-lookup tools.

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?

It explicitly states the intended use cases: market analysis, competitive benchmarking, and sales territory planning on the French market. It does not name alternative tools or say when not to use it, so it stops short of a 5, but the context is clear.

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