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veto_sre_advisor

Read-only

Calculate SLO error budget remaining and projected exhaustion, then receive ranked reliability improvements based on deterministic analysis.

Instructions

Calculates SLO error budget status (remaining %, projected exhaustion) and returns ranked reliability improvements. Error budget math is deterministic; prioritization uses the local agent loop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
incidentsNoOptional — recent incidents.
slo_targetYesSLO target % (e.g. 99.9).
window_daysYesMeasurement window in days (e.g. 30).
service_nameNoOptional — name of the service.
agent_responseNoPhase 2 response from the host AI (JSON). Pass this back when prompted by the server to complete the agentic loop.
downtime_minutesYesTotal downtime minutes in the window.
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the tool is read-only. The description adds context by stating the computation is deterministic and that prioritization uses a local agent loop, which is informative. It also hints at a multi-step process via the agent_response parameter. No contradictions with annotations.

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?

Two sentences with no wasted words. The first sentence front-loads the core purpose, and the second adds essential behavioral details. Structure is efficient and clear.

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 tool has 6 parameters, nested objects, and no output schema. The description lacks detail on the return format or structure of the 'ranked reliability improvements.' It also does not explain multi-step interaction flow despite the agent_response parameter. Given complexity, more completeness is needed.

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 100%, so the schema already documents each parameter. The description adds value by explaining the agent_response parameter's role as a 'Phase 2 response' to complete the agentic loop, which goes beyond the schema description.

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 clearly states the tool 'calculates SLO error budget status' and 'returns ranked reliability improvements,' using specific verbs and resources. This distinguishes it from the many sibling tools, none of which directly relate to SLO error budget calculations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for SRE-related tasks but does not explicitly specify when to use it over alternatives or what conditions warrant its invocation. It mentions deterministic math and a local agent loop but offers no usage guidance.

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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