Capture explicit human approval or rejection of accessibility findings after review, preserving decisions for audit and enabling follow-up actions.
Record an explicit human decision after the human has reviewed the report. Only call this tool in response to a clear human approval or rejection; it is not permission for the agent to decide on its own.
| Name | Required | Description | Default |
|---|---|---|---|
| note | No | ||
| decision | Yes | ||
| session_id | No | ||
| finding_ids | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully clarifies that the tool records a human decision and must not be used for autonomous agent decisions, but it does not disclose side effects, persistence, validation behavior, or return values. This is adequate but leaves significant behavioral gaps.
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 two concise sentences with no filler. The core purpose and usage constraint are front-loaded, making the tool's intent immediately clear without requiring the reader to parse unnecessary detail.
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 has 4 parameters, no output schema, and no annotations, yet the description only covers the decision aspect and the triggering condition. An agent still lacks guidance on what finding_ids refer to, what session_id means, how the note field is used, and what the tool returns or changes. This is incomplete for correct invocation.
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 for the bare parameter names. It only hints at 'approval or rejection' corresponding to the decision enum, but gives no meaning for finding_ids, session_id, or note. The description does not sufficiently explain how to populate the required parameters.
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 uses a specific verb ('Record') and resource ('explicit human decision') and clearly distinguishes the tool from the sibling analysis/review tools by stating it logs human approval or rejection rather than performing analysis. This gives an agent a precise understanding of the tool's role.
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 explicitly states when to call the tool ('Only call this tool in response to a clear human approval or rejection') and what it is not for ('not permission for the agent to decide on its own'). This is direct, actionable guidance with clear exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.