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GonzaloTorreras

ai-dememory

Ignore False Positive

memory.false_positive_ignore

Suppress false-positive secret-scan alerts by recording reviewed suppression decisions in a .ai-dememory-ignore.toml file.

Instructions

Record a reviewed secret-scan false-positive suppression in .ai-dememory-ignore.toml.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
reasonYes
reviewerYes
recommendation_idNo
review_after_daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
pathYes
ignoredYes
reviewerNo
review_dueNo
reviewed_atNo
review_afterNo
recommendation_idNo
recommendation_pathNo
review_after_statusNo
recommendation_actionNo
canonical_memory_updatedYes
recommendation_policy_violationNo
Behavior2/5

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

The description states it records a suppression (write operation), consistent with readOnlyHint=false. However, it does not disclose effects like overwriting existing entries, idempotency, or impact on future scans. The output schema exists but its content is unknown. Minimal behavioral insight beyond the obvious.

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

Conciseness4/5

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

The description is a single, concise sentence that front-loads the action and resource. While efficient, it omits necessary details (like parameter explanations) that could be structured in a bulleted list. Still, no wasted words.

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

Completeness2/5

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

Despite having 5 parameters, 0% schema coverage, and being a write operation, the description only provides the high-level purpose. It lacks parameter semantics, usage context, and return value description. Incomplete for effective tool selection and invocation.

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

Parameters1/5

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

Schema description coverage is 0%, yet the description provides no explanation for any of the five parameters (id, reason, reviewer, recommendation_id, review_after_days). The description fails to add meaning beyond the parameter names and types, leaving the agent unaware of their purpose or valid values.

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 action ('record'), the resource ('reviewed secret-scan false-positive suppression'), and the destination file ('.ai-dememory-ignore.toml'). It distinguishes from sibling tools like memory.false_positive_unignore and memory.review_false_positives by specifying the operation is for reviewed suppressions.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., memory.review_false_positives for reviewing, memory.false_positive_unignore for un-ignoring). No prerequisites mentioned (e.g., must have reviewed the false positive). The description implies 'reviewed' but does not explicitly state that this tool should only be used after review.

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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