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

No annotations are provided, so the description must disclose behavioral traits. It fails to mention that this is a write operation, requires perform_as, or relies on confirm/dry_run controls. It provides no information about side effects, notifications, or errors. The phrase 'for recruiting operations' is too broad to inform an agent of what happens when this tool is called.

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 sentence and is front-loaded with the endpoint, so it is short and does not waste words. However, it is under-specified and lacks actionable details, making it closer to a placeholder than a useful description. It does not earn its place because it adds little beyond the tool name and endpoint.

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?

This is a complex mutation tool with 7 parameters, a nested body object, and no annotations or output schema. The description is far too sparse to guide an AI agent; it omits expected return values, error behavior, prerequisites, and examples. An agent cannot understand what the tool does, when to use it, or how to construct the body from the description alone.

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%, which meets the baseline of 3 even though the tool description adds no parameter information. The schema itself gives some meaning to each parameter, but the 'body' parameter is described only as 'JSON body to send to the documented Lever endpoint,' which is vague and does not explain the required structure. Nevertheless, the description does not need to compensate given the high coverage.

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 references the Lever API endpoint 'POST /opportunities/:opportunity/feedback' and mentions recruiting operations, which gives some indication of the tool's scope. However, it does not explicitly state that this creates an opportunity feedback record, relying on the tool name and HTTP method for that inference. It also does not distinguish this from sibling tools like create_opportunity_note or create_feedback_template, leaving the purpose vague.

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?

No guidance is provided about when to use this tool versus alternatives such as create_opportunity_note or update_opportunity_feedback. The description only says 'for recruiting operations,' which is generic and gives no context about prerequisites, candidate selection, or when not to use it. There is no mention of exclusions or preferred alternatives.

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