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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds value by disclosing the returned content: databases, tables, and join keys, plus the ticker/date keying convention for stock tables. It doesn't mention output size or pagination, but with zero parameters that is a minor gap.

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 short sentences, zero waste, and the core scope is front-loaded before the domain enumeration and the relationship to query_warehouse.

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 no-parameter discovery tool with annotations covering safety and no output schema, the description explains the return content well enough to call it correctly. Only the output shape/size is left implicit, which is a small gap.

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 there is nothing for the description to disambiguate; baseline 4 applies. The description does usefully scope the returned content to what an agent will find.

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 precisely what the tool returns — every database and table the warehouse exposes, plus the join_keys columns each table joins on — and enumerates concrete domains (stocks, crypto, sports odds, weather, prediction markets, commodities, FRED). 'The map for query_warehouse' distinguishes it clearly from the sibling that consumes this metadata.

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 map for query_warehouse' establishes the intended usage context: call this to learn the schema before querying. It gives clear context but no explicit exclusions or when-not-to-use conditions.

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