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update_finding_status

Update a finding's validation status after manual verification, marking it as confirmed, false positive, or reset to unconfirmed for re-verification.

Instructions

Update the validation status of a finding.

Called by a validation agent after manually verifying a finding. finding_id: the finding's id (from list_unconfirmed_findings) status: 'confirmed' — finding is real and exploitable 'false_positive' — finding is not real, exclude from report 'unconfirmed' — reset back to pending (if re-verification needed)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
finding_idYes
Behavior4/5

Does 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 explains the consequences of each status value ('confirmed' means real/exploitable, 'false_positive' excludes from report, 'unconfirmed' resets to pending), which goes beyond the bare schema. It also clarifies the source of finding_id, adding useful operational context. However, it does not mention potential side effects, permissions, or failure modes, keeping it from a perfect score.

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 well-structured and front-loaded: the main action is stated in the first sentence, followed by usage context, then parameter details. Each line adds necessary information without redundancy. It is appropriately sized for the tool's simplicity.

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?

For a simple two-parameter update tool with no output schema and no annotations, the description covers the essential aspects: purpose, when to call it, and parameter semantics. It does not mention return values or what happens on failure, but these are not critical for such a focused operation. The overall context is sufficient for an agent to invoke it correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully explain both parameters. It does so excellently: finding_id is described as coming from list_unconfirmed_findings, and status is given a detailed breakdown of every accepted value with its semantic meaning. This provides complete guidance beyond the raw schema.

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's verb ('Update') and resource ('validation status of a finding'), making its primary function unambiguous. It distinguishes itself from sibling tools by being the only one that modifies finding status, and references list_unconfirmed_findings as the source of finding IDs, reinforcing its specific role.

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 provides explicit context for when to use the tool: 'Called by a validation agent after manually verifying a finding.' It implies the tool is for post-verification updates and offers no explicit alternatives or exclusions, but the usage context is clear enough for an agent to decide appropriately.

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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