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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the return format: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This goes beyond annotations by describing the output structure and usability, though it doesn't cover rate limits or authentication.

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 and focused. It opens with a clear verb+resource statement, then lists supported data domains, and ends with the return format and usage hint. However, the list of data sources is somewhat dense and could be more structured (e.g., bullet points), but it remains informative without being verbose.

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 complexity (discovery of many tools) and lack of output schema, the description fully explains what the tool returns (names, descriptions, schemas, examples) and how to use the results ('ready to call directly'). It also covers the limit parameter. No gaps are evident for an agent to understand the tool's purpose and invocation.

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 baseline is 3. The description does not add significant meaning beyond the schema; it mentions the main 'query' parameter and provides example queries (e.g., 'analyze housing market trends') but doesn't elaborate on parameter nuances. The schema already documents aliases and limits adequately.

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: 'Find tools by describing the data or task.' It lists many specific data sources (SEC filings, financials, FDA drugs, etc.) and explains that it returns top-N relevant tools with full schemas, ready to call. This distinguishes it from sibling tools that perform specific tasks (e.g., deep_research, validate_claim) by positioning it as a meta-discovery tool.

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 explicitly advises: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also lists example use cases (e.g., 'look up FDA drug approvals'). While it doesn't explicitly state when NOT to use it, the guidance is strong and suggests it is the initial step before selecting a specific tool.

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