Fetch aggregate guardrail outcome counts for LLM interactions, optionally filtered by agent, to track policy enforcement totals without category breakdown.
Guardrail outcome totals across LLM interactions, optionally scoped to one agent. Same underlying data as get_guardrail_interaction_counts, without the per-category breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | No |
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Outcome totals' implies an aggregate read-only operation and the data relationship to a sibling is disclosed, but there is no mention of return shape, time-range defaults, pagination, or whether counts are cumulative or windowed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with zero filler; the scope statement comes first and the sibling differentiation second. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity aggregate tool with no annotations and no output schema, the description is serviceable but thin: it never indicates what a 'total' looks like in the response, over what time window it is computed, or how results are ordered/limited. An agent could call it correctly but cannot predict the payload.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the single undocumented agent_id parameter. It does explain that the parameter is optional and that supplying it scopes results to one agent, though it adds no format/details (e.g., ID style) beyond that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb-plus-resource ('Guardrail outcome totals across LLM interactions') with an explicit scope modifier. It directly names and differentiates itself from the sibling get_guardrail_interaction_counts by stating what it omits ('without the per-category breakdown').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The comparison to get_guardrail_interaction_counts gives clear routing context: pick this one when you don't need category-level granularity. There is no explicit 'when not to use' or prerequisite guidance, but the alternative is named and the trade-off is spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.