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add_agent_reply

Add a reply to an annotation thread to ask clarifying questions or note findings on visual feedback pins.

Instructions

Add a reply to an annotation thread (e.g. to ask a clarifying question or note a finding).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesThe reply message body
authorNoAuthor name (defaults to "AI Agent")
annotationIdYesThe annotation ID
Behavior2/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states that a reply is added, implying a creation operation, but fails to mention critical details such as required permissions, whether the operation is idempotent, any side effects (e.g., notifications), or the response format. This lack of transparency increases the risk of misuse.

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?

The description is a single sentence that efficiently conveys the tool's purpose and a usage hint. Every word serves a purpose, with no redundancy or irrelevant details. This makes it quick for an AI agent to parse and understand.

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

Completeness3/5

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

For a simple tool with three parameters and no output schema, the description provides the essential purpose and usage hint. However, it omits what the tool returns (e.g., the created reply object or a success indicator), which would help the agent confirm the action's result. Given the tool's simplicity, the gap is moderate.

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?

The input schema has 100% description coverage for all three parameters, so the schema already documents them sufficiently. The description adds no additional semantic context about the parameters, neither clarifying defaults (like author defaults to 'AI Agent') nor explaining allowed values. Under high schema coverage, a baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the verb ('Add') and the resource ('reply to an annotation thread'), and provides concrete examples ('ask a clarifying question or note a finding'), making the tool's purpose clear. However, it does not explicitly distinguish this tool from similar siblings like 'add_bot_reply', which may share the same resource but differ in actor or intent.

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

Usage Guidelines3/5

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

The description gives an example of when to use the tool ('to ask a clarifying question or note a finding'), implying a conversational or investigative context. But it does not specify when not to use it, or mention alternative tools (e.g., add_bot_reply) for bot-generated replies, leaving the agent with limited guidance on tool selection.

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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