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

Annotations already declare readOnlyHint=true and idempotentHint=true, so readers know it's a safe, read-only operation. The description adds valuable behavioral context: it returns top-N results with full input schemas and curated examples, and explicitly notes that results are 'ready to call directly, no second schema lookup needed.' This gives the agent a sense of what the response will look like and how to act on it.

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 front-loaded with the core purpose, then expands into usage context, return behavior, and priority guidance. It is slightly longer than the TDQS high example but every sentence adds value—no filler. The structure guides the agent from 'what' to 'when' to 'what to expect.'

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 no output schema, so the description carries the burden of explaining return values, which it does well ('returns the top-N most relevant tools with names, descriptions, and full input schemas'). Given the tool's complexity (a meta-tool with many sibling tools), the description effectively covers purpose, usage, and output format, leaving little ambiguity for an agent.

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%, with all six parameters documenting their meaning (query, aliases, limit). The description reinforces that the primary input is a natural language description of data/task and enumerates example domains, but it does not add new details beyond the schema. The schema already provides the parameter semantics, so a baseline of 3 is appropriate.

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 opens with 'Find tools by describing the data or task,' which clearly identifies the tool's purpose (discovering other tools based on a natural language query). It distinguishes discover_tools from siblings by framing it as a meta-tool for browsing the available option set, not a domain-specific 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 states when to use the tool ('Use when you need to browse, search, look up, or discover what tools exist') and provides a clear directive ('Call this FIRST when you have many tools available and want to see the option set'). It contrasts with 'not just one answer,' implying that for direct answers the agent should use a domain-specific tool instead.

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