submit_lead_feedback

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

There are no annotations provided, so the description carries the full burden of behavioral disclosure. It doesn't state whether feedback is fully async, whether it affects a specific lead search job (implying the jobId context), whether repeated feedback for the same lead overrides prior feedback, or whether there are any limits on feedback frequency. For a mutating tool with zero annotation coverage, this is a meaningful gap.

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, efficient sentence with zero wasted words. It states the action, the resource, the polarity options, and the purpose all in one compact structure. This is appropriately sized for a simple feedback tool.

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?

The tool is relatively simple with only 3 parameters and no output schema, so the completeness bar is moderate. The description covers the what and the why, but given no annotations, it would benefit from noting whether feedback is tied to a specific AI output instance, is batch-able, or has any rate/duplicate constraints. For a feedback submission tool, the behavioral outcomes (what happens after submission) are somewhat underspecified.

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%, so all three parameters (jobId, leadEmail, feedback) are documented in the schema. However, the description adds context that feedback applies to an 'AI-generated lead message,' which clarifies that this is not generic feedback but specifically about the message output. This adds marginal value beyond the schema, which just labels 'feedback' as 'Feedback sentiment.' A baseline 3 is appropriate since the schema handles parameter documentation.

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 verb (submit) and resource (lead feedback on an AI-generated lead message). It specifies positive or negative feedback and the purpose (improve future output quality). It doesn't explicitly distinguish from siblings, but no sibling clearly overlaps with this feedback submission function, so differentiation isn't critical here.

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 implies when to use this tool - when providing feedback on an AI-generated lead message - but there are no explicit exclusions or alternatives mentioned. Given the sibling list contains preview_message and related AI message tools, some guidance about when feedback is relevant would help, but the context is reasonably inferred from the description.

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