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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds valuable behavioral context: returns top-N results with names, descriptions, full input schemas, and curated examples, and results are directly callable without a second lookup. It also advises calling this first for broad exploration. This goes beyond the annotations and enriches the agent's understanding of the tool's behavior.

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, front-loaded with the primary purpose, and every sentence earns its place. It lists relevant domains efficiently, explains the return format and the 'call first' advice. There is zero fluff, and the structure is logical: purpose → when to use → what you get.

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?

The tool has 6 parameters (with 5 aliases), 100% schema coverage, and no output schema. The description adequately explains what the tool returns (top-N tools with schemas and examples) and gives context for the alias system. It covers the discovery purpose and the direct-callability advantage, making it sufficiently complete for the agent to use correctly without additional details.

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 the baseline is 3. The description adds meaning by providing concrete examples ('look up FDA drug approvals', 'analyze housing market trends') and explicitly mentions that the query parameter accepts aliases (task, q, description, search), which clarifies the intended usage. It also reinforces the limit parameter with 'top-N'. This adds value beyond the schema descriptions.

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 uses a specific verb ('find') and resource ('tools'), and distinguishes itself from sibling tools by emphasizing discovery across many domains (SEC filings, FDA drugs, etc.) and noting the output includes schemas and examples. This makes its role as a meta-search/discovery tool unmistakable.

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 gives explicit when-to-use guidance: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It does not explicitly name alternative tools to use instead, but the 'not just one answer' phrasing implies the tool is for exploring options rather than retrieving a single result. This is clear enough to guide the agent.

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