Read naming generation provenance

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It states read-only, paginated, free, requires the naming:read scope, and enumerates return fields including credit reservation/consumption. This is comprehensive for a read-only list tool.

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 well-structured sentences: the first lists what the tool returns, and the second provides use cases and key properties. Every sentence delivers useful information with no redundancy.

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?

Despite lacking an output schema, the description effectively documents return values by listing all major fields (fingerprint, recipes, model, credits, status, timestamps). It also covers pagination, permissions, and cost implications, making the tool's behavior fully understandable.

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 description coverage is 100%, so the baseline is 3. The description adds context like 'for one project' and 'paginated' but doesn't explain the parameters themselves beyond what the schema already provides. No additional semantics are introduced.

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 the tool reads the generation ledger for one project, listing every generate_names run with specific data fields. This specific verb+resource+scope distinguishes it from sibling list tools like list_naming_recipes and list_naming_projects.

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 gives two use cases: tracing where a set of names came from and checking if a failed run charged credits before retrying. While it doesn't name alternative tools directly, the context strongly implies when this tool is appropriate in relation to other listing tools.

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