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

With no annotations provided, the description carries the full burden and does well by explaining the data provenance (registry approval dates), the historyQuality flag with detailed meanings, and the caveat that current_state_only markets undercount withdrawn products. This goes beyond a simple 'get' and informs the agent about data quality and interpretation.

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 three sentences long and packs a lot of information: the metric, data provenance, data-quality caveats, and use cases. Each sentence contributes meaning without fluff, though the density may require careful reading. It is concise but not minimal.

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 6-parameter tool with no output schema, the description covers the core concept, historical depth, the historyQuality flag, and intended analytical use cases. It does not detail the return format or multi-competitor comparison mechanics, but it provides enough context for an agent to understand what the tool offers and its limitations.

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?

All 6 parameters already have schema descriptions, so the baseline is 3. The description does not add parameter-specific guidance, such as the relationship between name, ticker, and manufacturer_id, but it does add context about the output's historyQuality flag, which relates to how the data is generated.

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 defines the tool's output as new registrations per year by market, and distinguishes it from siblings by noting it is built from registry approval dates and reaches back as far as 56 years. Although it lacks an explicit verb like 'returns', the noun phrase is specific and unambiguous about the resource and metric.

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 'Use for entry velocity, launch cadence, and comparing two competitors' expansion over time,' which gives clear context for when to select this tool. It does not name alternative tools or exclusions, but the use cases are sufficient for an agent to differentiate it from a generic listings tool.

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