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feedback_request_email

Write a short email asking a client for private feedback after a project to improve your service and uncover patterns you would not discover otherwise.

Instructions

Write a short, genuine email asking a client for private feedback after a project — not a public testimonial, just honest input to help you improve. Clients who are asked for feedback feel valued; you get patterns you'd never discover otherwise. Distinct from testimonial_request (which asks for a public review for marketing purposes). Does not count against your monthly draft limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
client_nameYesThe client's first name
project_nameYesThe project you delivered (e.g. 'the website redesign', 'the brand identity project', 'the three-month content retainer')
specific_aspectNoOptional: a specific part of the experience you're genuinely curious about — makes the request feel purposeful rather than generic (e.g. 'how the communication felt during the revision rounds', 'whether the timeline worked for your team', 'the clarity of my initial briefing process')
your_nameNoOptional: your name for the sign-off
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the email is short and genuine, asks for honest input, and notes it does not count against the monthly draft limit. While it could mention delivery method or timing, it adequately communicates the tool's non-destructive behavior and purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: first states purpose and scope, second adds benefit, third distinguishes from sibling and adds a constraint. Every sentence earns its place with zero waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema or annotations, the description covers purpose, usage context, and a key behavioral trait. It doesn't explain delivery or confirmation, but for a simple email generation tool, it provides sufficient information for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with well-described parameters. The description adds context (e.g., optional specific_aspect makes the request purposeful), but does not significantly extend beyond the schema. Baseline 3 is appropriate since the schema already does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a feedback request email after a project, emphasizing it is for private feedback, not a public testimonial. It uses a specific verb ('write') and resource ('email'), and explicitly distinguishes from the sibling 'testimonial_request' tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies when to use (after a project, for private feedback) and contrasts with testimonial_request for public reviews. It could be improved by explicitly stating scenarios where this tool should not be used, but the sibling differentiation provides strong guidance.

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

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