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

With no annotations, the description carries the full burden and does well: it discloses the 2-minute polling cadence, the possible result values (operational | degraded | down | unknown), and the last-checked timestamp, which warns the agent about staleness. It omits auth requirements and error behavior, but the disclosure of freshness semantics is substantive.

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?

Three tight sentences, front-loaded with the resource and scope, followed by freshness cadence, then the return contract. Every sentence carries information an agent needs.

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?

No output schema and no annotations exist, so the description must cover the return contract, and it does: enumerated values plus the last-checked field. For a zero-param read tool this is sufficient to invoke and interpret it 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 tool takes zero parameters, so per the rubric the baseline is 4. The description adds no parameter confusion and the empty schema is self-consistent with the global 'every major AI service' scope.

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?

States a specific verb (Get) and resource (live operational status of every major AI service), and names the concrete set of services tracked, which distinguishes it from siblings like check_free_tier_status or check_ai_supply_chain_risk. An agent can identify the tool's output without opening the schema.

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 gives clear context (live operational status, polled every 2 min) and points to /api/status for per-component detail, effectively routing granular queries elsewhere. It stops short of explicitly naming when not to use this tool or a sibling alternative.

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