Service status and availability

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

The description add meaningful behavior beyond annotations: the availability target is best-effort and has no service credits, and the response explicitly states this rather than implying it. It also reveals a critical behavioral truth about how dataset availability affects sibling tools.

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 dense but every sentence earns its place: hitting scope, time windows, target expectations, dataset availability behavior, and an important caveat. It wastes no words on redundant restating of the title.

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 zero-parameter, read-only status tool with an output schema, nothing important is missing. The description is sufficient for an agent to invoke the tool and correctly interpret what the dataset check enables.

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?

With zero parameters, there are no parameter semantics the description must clarify. The description still helps by stating what the tool reports, giving the agent a complete idea of the response's meaning without relying on the output schema alone.

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 states a specific verb and resource: it reports service uptime, historical availability over 24 hours/30/90 days, target expectations, and which optional datasets are built. It distinguishes itself by positioning get_status as the pre-check for get_interlinear and get_connections, whose failure mode is explicitly named.

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?

It explicitly tells the agent when to use this tool: check get_status before calling get_interlinear or get_connections, since those tools fail with `dataset_unavailable` when the dataset is absent. That is concrete, actionable guidance for an otherwise simple status tool.

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