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

The description goes beyond the all-false annotations by explicitly disclosing the 'lazily generate' behavior, meaning this tool may trigger computation or creation rather than only reading stored data. It does not detail caching behavior, latency, or potential costs, but the key non-read behavior is visible.

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 a single, tightly written sentence that front-loads the main action and scopes it to a single mention. Every part adds meaning, with no wasted words or repetition of schema fields.

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?

For a one-parameter tool with an output schema, the description covers the core purpose, scope, and the notable lazy-generation behavior. It does not mention edge cases like invalid mention IDs or when to choose this over siblings, but the low complexity and existing schema make this acceptable.

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 100%: the only parameter, mentionId, is fully documented with its type and format. The description adds only the 'single mention' scope and does not replicate or extend parameter details, so a baseline score 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 clearly states the tool's purpose: retrieving (and lazily generating) the AI relevance reasoning and tags for a single mention. It uses a specific verb and resource, and the 'why it was scored the way it was' framing distinguishes it from listing or updating mention tools.

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?

Usage context is implied: use this when you need the reasoning or tags behind a single mention's score. However, no explicit alternatives, conditions, or when-not-to-use guidance are provided, leaving the agent to infer the right scenario.

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