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delimit_security_deliberate

Triage security findings using multi-model deliberation to classify each as real risk, false positive, accepted risk, or needs immediate action. Accepts ledger or JSON input.

Instructions

Multi-model triage of security findings (Pro).

Runs deliberation on ingested security findings to classify each as: real risk, false positive, accepted risk, or needs immediate action.

Can work on findings from the ledger (auto) or from a JSON string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
findingsNoJSON string of findings to triage, or empty to pull from ledger.
repoNoRepository context for the triage.
focusNoWhich findings to triage - "critical", "high", "all". Default: critical.critical

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description must cover behavioral traits. It reveals the classification outcomes and the ability to work from two sources, but it does not disclose side effects (e.g., whether it modifies the ledger), required permissions, or rate limits. The term 'deliberation' suggests a non-destructive analysis, but this is not explicit.

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 three sentences with no wasted words. It front-loads the core purpose, lists classifications, and notes data sources. Each sentence adds essential information, making it efficient for an agent to parse.

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?

Given the tool complexity (security triage with output schema) and complete schema coverage, the description covers purpose, classifications, and data sources. It lacks usage guidance and safety details, but the existence of an output schema and full parameter descriptions compensate. It is mostly complete for an agent to select and invoke.

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 description coverage is 100%, so the baseline is 3. The description adds context for the 'findings' parameter (empty means pull from ledger) and mentions the 'focus' parameter's default, but these are already covered in the schema. The description does not add substantial new information about parameter meaning beyond what the schema provides.

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 action: 'multi-model triage of security findings' and enumerates the specific classifications (real risk, false positive, accepted risk, needs immediate action). It also specifies the data sources (ledger or JSON string), distinguishing it from sibling tools like delimit_security_scan (which likely discovers findings) and delimit_deliberate (a more generic deliberation tool).

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 implies the tool is used after findings are ingested, but it does not explicitly compare to alternatives or state when not to use it. No exclusions or conditions are provided, leaving the agent to infer context from the tool name and siblings.

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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