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audit_citations

Audit a lesson plan to ensure certified claims cite existing claim-cards and every claim has a tier label, returning a report of issues.

Instructions

Advisory self-check for a lesson before you teach it. Submit your LessonPlan and this verifies the ONE boundary that matters: a claim you marked tier="certified" must cite an existing certified claim-card (so you never present a simulation-verified number you don't actually have), and every claim must carry a tier label (so an interpretive mechanism is never disguised as certified fact). It is NOT a hard gate and judges nothing else — your lesson's structure, ordering, depth, and selection are entirely yours.

    Args:
        lesson_plan_json: JSON of {topology_class, spec_id?, title?, claims:[{text, tier:
          "certified"|"interpretive", cites: claim_card_id (required when certified)}]}.

    Returns:
        JSON AuditReport {passed, certified_total, certified_ok, interpretive_total,
          findings:[{index, ok, reason}]}.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lesson_plan_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully carries the transparency burden. It discloses that the tool is advisory, not a hard gate, verifies only the citation boundary, and does not evaluate structure, ordering, depth, or selection. It also states exactly what conditions are checked (certified claims must cite, all claims must have tiers), giving the agent complete behavioral expectations.

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 somewhat wordy with the Args/Returns docstring format, but every sentence provides valuable information. The structure is logical and front-loaded with the core purpose, followed by scope exclusions and the input/output spec. The length is justified by the tool's complexity, though a slightly tighter phrasing could improve conciseness.

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 description covers the full context: when to use it, what it verifies, what it ignores, the exact input format, and the return report structure. Even though an output schema exists, the description's explanation of the return fields and their meaning helps the agent understand the tool's behavior without needing to parse the schema separately. No significant gaps remain.

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?

The input schema is minimal (just a string parameter with 0% description coverage), so the description must compensate. It provides the complete internal JSON structure, including required fields, allowed tier values, and the condition for cites. This fully compensates for the lack of schema-level detail, adding essential meaning beyond the parameter name.

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 uses a specific verb ('verifies') and resource ('claim citations in a lesson'), clearly identifying the tool's function. It differentiates itself from siblings by focusing on the one boundary that matters and explicitly stating it is not a hard gate, making its unique purpose unmistakable.

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 clear context for when to use it ('Advisory self-check for a lesson before you teach it') and explicitly states exclusions ('judges nothing else'), covering when-not. However, it does not name alternative tools or explicitly say 'use this instead of X', preventing a perfect score.

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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