Drop Beacon

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

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, closed-world), so the bar is lower. The description adds some context by specifying the types of data returned (e.g., sell-through rates, top brands), but does not disclose additional behaviors like rate limits, authentication needs beyond the optional api_key in schema, or response format details. No contradiction with annotations exists.

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 appropriately sized with two sentences: the first front-loads the core functionality and data points, and the second provides usage context. Every sentence adds value without redundancy, though it could be slightly more structured (e.g., bullet points for clarity).

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 tool's moderate complexity (3 parameters, no output schema), annotations provide safety and idempotency info, and schema covers parameters fully. The description adds purpose and usage context but lacks details on output format, error handling, or data freshness, leaving some gaps for an agent to invoke it correctly without trial and error.

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%, providing full parameter documentation. The description does not add any semantic details beyond the schema, such as explaining interactions between parameters (e.g., how 'category' affects other outputs) or default behaviors. Baseline 3 is appropriate since the schema handles 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?

The description clearly states the tool's purpose with specific verbs ('Get') and resources ('EDC market trend data'), listing concrete data points like sell-through rates, top brands, price distribution, and new product counts. It distinguishes itself from siblings by focusing on aggregated market analysis rather than specific product searches, availability checks, or brand details.

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?

The description provides clear context for usage ('Useful for market analysis and understanding what's hot in the EDC space'), which implicitly guides when to use it. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, such as using 'search_products' for granular queries instead of aggregated trends.

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