generate_seed_data

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

No annotations provided, so description carries full burden. It discloses many behaviors: respecting constraints, handling types, per-column overrides, and execution mode. Lacks details on permissions or error handling, but covers the main traits.

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?

Single well-structured sentence with bullet-like enumeration. Front-loaded with purpose. No redundancy, though breaking into clearer sections could improve readability.

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?

Given 9 parameters and no output schema, description covers inputs, core behaviors, and output mode. Missing details on return value when apply is false and error scenarios, but overall complete for most use cases.

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?

Schema coverage is low (22%), and description adds meaning for key parameters (table, count, column_values, skip_fks, apply) but does not explain server, database, override_schema, or schema beyond their names. Partial compensation.

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 clearly states it generates schema-aware fake seed data for a table, listing many features that distinguish it from sibling tools (analyze, execute, etc.). The verb 'generate' and resource 'seed data' are specific.

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?

Implies when to use (need seed data) and mentions apply vs SQL-only via `apply: false`. Does not explicitly state when not to use or compare to alternatives, but the shared context makes it clear.

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