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log_consent_event

Record data processing actions in a consent ledger for audit trail. Logs agent activity with 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
Behavior4/5

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

With no annotations, the description carries the burden. It clearly states the tool appends a row (non-destructive write) for audit trail purposes, implying immutability. No contradictions.

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 concise with a clear structure: a one-line purpose, a usage guideline sentence, and a parameter list. No unnecessary words, front-loaded with the most important information.

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?

All 5 parameters are explained in the description, and the output schema (not shown but present) covers return values. The description provides sufficient context for correct usage.

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?

Despite 0% schema description coverage, the description includes a well-documented parameter list with explanatory comments for each argument, fully compensating for the schema's lack of descriptions.

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 starts with a specific verb+resource ('Append a row to the consent_ledger table') and clarifies the use case ('whenever an agent processes data'), making it distinct from sibling logging tools like log_agent_run or save_session_event.

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?

It explicitly states when to call ('whenever an agent processes data'), providing clear context. It does not list when not to use or alternatives, but the purpose is unambiguous enough to guide selection.

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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