get_collection_info

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

With no annotations, the description carries the burden of behavioral disclosure. It does an above-average job by noting that distinct values are 'sampled' (not exhaustive) and lists exactly what metadata is returned. It does not explicitly state that the operation is read-only, but 'Discover' and the absence of parameters strongly imply this; the sampling caveat is especially valuable.

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 two sentences long, front-loads the core purpose, and packs relevant details (schema contents, sampled values, usage guidance) without filler. Every sentence earns its place.

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 the tool's simplicity (no parameters), the presence of an output schema, and the clear usage context, the description is complete. It explains what the tool returns, why it exists, and when to invoke it relative to sibling tools.

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 parameter-semantics dimension has little to evaluate. Per the rubric, a zero-parameter tool receives a baseline of 4. The description adds no parameter-specific details because none exist, which is appropriate.

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 opens with a specific verb ('Discover') and clearly identifies the resource ('ACL NLP papers collection schema'), then enumerates what the tool reveals: vector dimensions, distance metrics, total paper count, and sampled distinct values for filterable fields. This differentiates it from sibling tools like search_papers and list_papers by positioning it as a schema-discovery tool.

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 description explicitly says 'USE THIS first' and ties the tool to concrete scenarios: when you need to know venues, years, or fields before building search_papers or list_papers filters. This provides clear guidance on when to use it and implies that the sibling tools are the follow-up alternatives.

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