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

Annotations only declare the write profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false), so the description carries real weight here and delivers: 'Identifying data is rejected; borderline content is held for human review' discloses moderation/validation behavior, and 'Requires API key' flags auth. It does not say whether reports are public or permanent, though 'so others see' hints at visibility.

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?

Four dense sentences, front-loaded with purpose then constraints. No filler sentences, though the privacy and API-key clauses are packed tightly enough that structure could be cleaner.

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 12-parameter mutation tool with no output schema, the description covers purpose, auth, privacy rejection behavior, and the intent of the optional provenance fields. It omits what a successful report returns and the semantics of the scoring/metric fields, leaving a modest gap.

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 67% across 12 parameters. The description explains the intent of model/client/tool_versions ('so others see what it was tested on') and adds privacy rules for notes, but says nothing about outcome, rating, metric_value/metric_unit, improved_recipe, or improvement_note. Baseline 3 is appropriate given partial schema coverage and partial description compensation.

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?

States a specific verb and resource with scope: 'Report what happened after trying a downloaded recipe.' An agent can tell this is a feedback/reporting tool, but the description never distinguishes it from the sibling quick_report, which appears to be an alternative reporting path.

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?

Gives clear triggering context ('after trying a downloaded recipe') and points to the token source (download_recipe). It also sets expectations for honesty and failure reporting. It does not name quick_report as the lighter-weight alternative, so routing between the two report tools is left to inference.

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