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Delimit Audit

delimit_audit

Audit code changes through security, correctness, and governance lenses, using multiple models to surface consensus and tradeoffs for high-confidence review.

Instructions

Cross-model code audit — 3 models, 3 lenses, synthesized (Pro).

When to use: for high-confidence review of a code change, where agreement across models is the signal and disagreements surface tradeoffs. When NOT to use: for raw multi-model debate (use delimit_deliberate) or single-model review (delimit_review).

Sibling contrast: delimit_review is single-prompt multi-model; delimit_deliberate is full debate; this is structured cross-lens audit (security / correctness / governance).

Side effects: gated by require_premium. Calls models via ai.cross_model_audit.audit. No ledger write — caller decides what to do with findings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lensesNoComma-separated lenses — "security", "correctness", "governance". Empty = all three.
targetNoFile path, git diff output, or code snippet to audit. Required.
target_typeNo"file" (default — reads file), "diff" (git diff text), or "snippet" (inline code).file

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changedv4.7.9
    • changedInput schema / properties / lenses / description
      Previous value: -"Comma-separated lenses to apply (security, correctness, governance). Default: all."New value: +"Comma-separated lenses — \"security\", \"correctness\", \"governance\". Empty = all three."
    • changedInput schema / properties / target / description
      Previous value: -"File path, git diff output, or code snippet to audit."New value: +"File path, git diff output, or code snippet to audit. Required."
    • changedInput schema / properties / target_type / description
      Previous value: -"\"file\" (reads file), \"diff\" (git diff text), \"snippet\" (inline code)."New value: +"\"file\" (default — reads file), \"diff\" (git diff text), or \"snippet\" (inline code)."
  2. Addedv4.5.5

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses side effects beyond annotations: it is gated by require_premium, calls models via ai.cross_model_audit.audit, and performs no ledger write. This gives the agent important context about cost, availability, and persistence behavior that the annotations do not convey.

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 with clear headings and front-loaded purpose. Every sentence adds value—usage context, sibling contrast, or side effects—without unnecessary fluff.

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

Completeness5/5

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

The definition is complete for selecting and invoking the tool correctly: purpose, routing, side effects, and sibling relationships are all covered. An output schema exists, so the lack of return-value detail in the description is acceptable.

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?

Parameter schema coverage is 100%, so the schema already documents all parameters with defaults and allowed values. The description reinforces the lenses but adds no substantially new parameter semantics 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 performs a cross-model code audit across three lenses with synthesized output. It explicitly distinguishes itself from sibling tools delimit_review and delimit_deliberate, making selection unambiguous.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use and when-NOT-to-use guidance, naming the exact alternatives (delimit_deliberate, delimit_review). The sibling contrast further clarifies how this tool differs from those alternatives.

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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