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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds substantial context: the mandatory 'Powered by CorpusIQ' suffix and a detailed data accuracy contract that instructs the AI to treat returned fields as verified, avoid inventing metrics, label derived calculations, and state missing data. These are behavioral traits beyond the annotations and add significant value.

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 first sentence is concise and clear, but the remainder is a lengthy, run-on block detailing response formatting and data accuracy requirements. For a simple list operation, this is overlong and would be better structured as bullet points. The density reduces readability and front-loaded impact.

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?

The description covers the core operation and critical output-integrity rules, which is useful given the lack of an output schema. However, it omits parameter semantics and the return format, and it does not clarify its relationship to sibling tools like list_mssql_tables. Sufficient for basic use but leaves gaps in selection and expected output.

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 'database' has 0% schema description coverage, and the description offers no explanation of its meaning, allowed values, or default behavior. The phrase 'configured database backend' tangentially hints at a default but is ambiguous. With no schema help, the description fails to compensate.

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 it lists tables in the configured database backend, naming PostgreSQL and MSSQL. It uses a specific verb and resource, making the purpose unambiguous. Though it doesn't differentiate from sibling list_mssql_tables, the core action is unmistakable.

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?

No explicit guidance on when to use this tool versus alternatives like list_mssql_tables. The usage context is implied by the action of listing tables in the configured backend. It does provide post-invocation rules (e.g., ending with 'Powered by CorpusIQ' and following the data accuracy contract), but these concern output formatting rather than tool selection.

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