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

Annotations already cover read-only/idempotent/non-destructive, but the description adds real context: it needs Kliqo sign-in, is read-only, and results are ordered 'most important first'. It could go slightly further on pagination/limit behavior, but the ordering and auth notes are the valuable non-annotation content.

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?

Front-loaded with what it returns, then filters, then usage examples, then auth note – a sensible order. The single dense paragraph packs a lot but every clause carries information; only mild cost is density over scannability.

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?

An output schema exists, so return-value explanation is optional, yet the description still summarizes the returned fields. Combined with filter semantics, auth requirement, and sibling routing, an agent has everything needed to invoke it correctly.

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 coverage is 88%, so most params are documented structurally. The description adds meaning the schema lacks by mapping the `filter` enum values to intents ('needs attention, wins, missing (brand not named), competitor_gap (a competitor named, you not)'), which is the key semantic gap since the schema merely says 'Which answers'.

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?

Specific verb+resource: 'List recent AI answers to the project's tracked prompts', and it immediately names the fields returned (prompt, model, brand mention type, rank, cited sites). It clearly differentiates from the sibling get_answer by saying 'open one with get_answer'.

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?

Explicit when-to-use examples ('where does ChatGPT not mention us?', 'show answers that recommend competitors') plus the escalation path to get_answer for detail. Filter values are enumerated with plain-English meaning, so the agent can pick the right call without opening the schema.

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