aleph_statistics

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavior beyond this: it returns aggregate statistics rather than specific records, and it contextualizes the open-world nature by warning against concluding absence before checking coverage.

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 sentences, no filler. The core capability is front-loaded, and the practical use case is stated succinctly in the second sentence. Every clause adds value.

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 statistics tool, the description is complete: it explains what the tool returns, how results are broken down, and when to invoke it. No output schema exists, but the aggregate breakdown is named, so an agent can make an informed call.

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, and schema description coverage is 100%, so there are no parameter semantics to explain. The description appropriately focuses on output semantics instead, which is the useful information for an agent selecting this tool.

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?

Description uses specific language—'Size and shape of the Aleph instance'—and states exactly what it returns: dataset and entity counts with breakdowns by type, category, and country. It also clearly communicates its role as a coverage-gauge tool, which distinguishes it from entity/search/collection tools among the siblings.

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 the agent when to use this tool: 'Use it to gauge coverage before concluding something is absent.' It does not explicitly list exclusions or name alternative tools, but the use case is distinct enough from siblings like search or entity retrieval that the guidance is sufficient.

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