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usage_events

Query observed usage logs to review route-through prompts and their risk findings. Filter by model, risk level, allowed status, and time range.

Instructions

[READ] Query the observed-usage log (route-through prompts + their findings).

Args: model: Filter by model name. risk_level: Filter by risk band (none/low/medium/high/critical). allowed: True = only allowed calls; False = only blocked. since: ISO start timestamp. limit: Max rows.

Returns {"events": [...], "count": N, "returned": N, "limit": L, "truncated": bool}. When truncated is true more prompts were observed than were returned — raise the limit or narrow the filters. Never conclude "no risky prompts were observed" from a truncated result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
modelNo
sinceNo
allowedNo
risk_levelNo
Behavior4/5

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

Discloses that it returns a specific data structure with truncated flag, and warns about result interpretation. Since no annotations are provided, the description carries full burden. It could mention that it is a read-only operation with no side effects, but that is implied by '[READ]'.

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 (~11 lines) and well-structured: first line states purpose, then parameter list, then return structure with a critical usage warning. Every sentence adds value with no redundancy.

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?

For a query tool with 5 optional parameters and no output schema, the description fully specifies the return format and explains truncation behavior. It covers all necessary context for an AI agent to use the tool correctly.

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?

Schema description coverage is 0%, so the description must explain parameters. It provides brief but meaningful descriptions for all 5 parameters (e.g., 'model: Filter by model name.', 'risk_level: Filter by risk band (none/low/medium/high/critical).'). Adds meaning beyond schema types and defaults.

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 '[READ]' and clearly states it queries the observed-usage log for route-through prompts and their findings. Verb 'Query' and resource 'usage log' are specific, and the sibling tools list includes other operations like scan_prompt, so purpose is distinct.

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?

Provides clear guidance on interpreting truncated results and warns against drawing conclusions from truncated data. However, it does not explicitly state when to use this tool versus alternatives like scan_prompt or anomaly_report.

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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