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

With no annotations, the description carries the full burden. It discloses that drafts are filtered out, describes the return format, and lists the fields returned. It could go further by noting ordering or potential absence of results, but it already provides meaningful behavioral context beyond a generic 'list' statement.

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 description is concise and well-organized. It leads with the core purpose, then usage guidance, then return shape, then a filter note. Every sentence contributes to understanding the tool 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?

For a simple, zero-parameter list tool, the description is complete. It covers purpose, usage, return format, and filtering. Since there is no output schema, the explicit field listing compensates adequately, making the tool self-explanatory.

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 has zero parameters, so the schema coverage is effectively 100%. The description adds value by explaining the meaning of the output fields and the filtering behavior, though it does not need to explain parameters that don't exist.

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 states a specific verb ('Returns a flat index') and resource ('every published OpenChainBench benchmark') with a clear list of included fields. It also distinguishes itself from the sibling `get_benchmark` by explicitly saying to use that tool for specific slugs.

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?

Explicit usage guidance is provided: 'Call this first when the user asks a discovery question' and then use `get_benchmark` for details. This clearly states when to use this tool versus the alternative, along with example questions.

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