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

Despite no annotations, the description thoroughly discloses behavior: it explains the dynamic market definition ('semantic neighbourhood of your natural-language query'), the measured nature of every field (with specific statistical outputs: median, mean, stdev, p25/p75, min–max, n), and the pricing benchmark's architecture. It also implicitly warns that the market is not a fixed category, which is a critical behavioral nuance.

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 concise for the amount of content it covers. It front-loads the core action and scoping, then lists expected outputs, and ends with a parameter guidance and sibling exclusion. No wasted sentences; every clause adds value.

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?

Given the complexity of the tool (dynamic market definition, multiple measured metrics), the description is remarkably complete. It covers what the tool does, how it's scoped, what outputs to expect, and when to use an alternative. The absence of an output schema is compensated by the description's detailed explanation of the response contents.

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?

Although the schema already covers 100% of parameters, the description adds semantic value by clarifying the `query` parameter's role as a natural-language capability or market with concrete examples, and explicitly describes the `response_mode` effect. It doesn't add format details for response_mode beyond the enum, but it does confirm defaults, which is helpful.

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 is unusually specific: it identifies a unique 'semantic neighbourhood' scoping (nearest providers by text embedding, no fixed category), names the resource (ONE market deep-dive), and clearly distinguishes from siblings like research_capability. The action ('Deep-dive ONE market') is precise with measurable outcomes listed.

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 states when to use: 'before building or investing'. Contrasts with the 'research_capability' sibling by stating that for 'market + pricing + a ready shortlist in one call, use research_capability'. This is clear guidance on when NOT to use this tool.

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