get_service_health

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

With no annotations, the description fully discloses behavior: read-only, no side effects. It also details the return structure including score, status, contributing metrics, log summary, anomalies, and correlations, plus the edge case for services with no data. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear main verb, usage guidance, prerequisites, behavior, and return details. Every sentence adds value without redundancy. It is efficient and front-loaded.

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?

Given the tool has only one parameter and no output schema, the description covers all necessary aspects: purpose, usage, prerequisites, behavior, return structure, and edge case. It is fully complete for an AI agent to use correctly.

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?

The only parameter 'service' already has good schema description, but the description adds crucial context: required, case-sensitive, and that the name must be exactly as returned by list_services with an example. This enhances clarity beyond the schema.

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?

The description clearly states the tool produces a single aggregated health verdict for one service by combining metrics and logs, and distinguishes it from sibling tools like query_metrics, query_logs, and detect_anomalies by specifying when to use each.

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 description explicitly tells when to use this tool ('fastest way to answer is this service healthy right now and why?'), when to use alternatives, and includes a prerequisite to get the exact service name from list_services. This provides complete usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.