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log_consent_event

Records a consent event to create an audit trail when an agent processes data, capturing action, classification, and session details.

Instructions

Append a row to the consent_ledger table.

Call this whenever an agent processes data so there is an audit trail
of what was processed, when, and under what classification.

Args:
    action:              Short description — e.g. 'scan_document', 'anonymize_patient_data'.
    data_classification: 'PUBLIC' | 'INTERNAL' | 'CONFIDENTIAL' | 'SENSITIVE'.
    agent_slug:          Which agent performed the action.
    notes:               Free-text context.
    session_id:          Current session identifier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
data_classificationNoPUBLIC
agent_slugNo
notesNo
session_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are present, so the description must carry the full behavioral burden. It only says 'append a row' without details on idempotency, side effects, error handling, permissions, or output behavior, leaving significant gaps.

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 extremely concise: a one-sentence intro, a one-sentence usage note, and a bullet list of parameters. Every sentence adds value without extraneous content.

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

Completeness3/5

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

The description covers purpose and parameter semantics adequately for a simple audit tool. However, it omits details about the return value (output schema exists but is not discussed) and error behavior, which are needed for full completeness given no annotations.

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

Parameters4/5

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

With 0% schema description coverage, the description adds essential meaning: it explains each parameter's role (e.g., action as 'short description', data_classification with allowed enum values), which the schema lacks. It uses practical examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'append[s] a row to the consent_ledger table' with a specific purpose: audit trail for agent data processing. However, it does not explicitly differentiate from similar logging siblings like log_agent_run.

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?

Explicitly states 'call this whenever an agent processes data', providing clear context for use. No exclusions or alternatives are mentioned, but the guidance is actionable.

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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