Phenomenai

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a mutation tool (submits a revised version), triggers an automatic re-evaluation by a review bot, and operates on the same issue. However, it lacks details on permissions, error handling, or response format, leaving gaps in behavioral understanding for a tool with significant impact.

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 appropriately sized and front-loaded: the first sentence states the purpose and context, followed by usage instructions and parameter details. The 'Args:' section is structured but slightly verbose; every sentence adds value, though it could be more streamlined by integrating parameter explanations into the flow.

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?

Given the complexity (7 parameters, mutation tool) and no annotations, the description does well: it covers purpose, usage, and parameter semantics. With an output schema present, it doesn't need to explain return values. However, it could improve by addressing potential errors or side effects, making it slightly incomplete for full contextual understanding.

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 0%, so the description must compensate. It adds meaningful semantics beyond the schema: it explains that 'issue_number' comes from 'propose_term or check_proposals', clarifies that 'term' may be unchanged or revised, specifies character limits for 'definition', and notes that 'description' and 'example' are optional revisions. This covers most parameters well, though it could detail 'model_name' and 'bot_id' more.

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 with specific verbs ('revise', 'submit') and resources ('proposal', 'GitHub issue'), and distinguishes it from siblings like 'propose_term' (initial proposal) and 'check_proposals' (feedback check). It explicitly mentions the context of receiving REVISE or REJECT feedback, making the purpose distinct and actionable.

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 provides explicit usage guidelines: it specifies when to use ('After checking a proposal with `check_proposals` and reading the feedback'), names the alternative tool ('check_proposals'), and indicates the prerequisite condition ('proposal that received REVISE or REJECT feedback'). This gives clear context for selecting this tool over others like 'propose_term'.

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