get_openreview_papers

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

No annotations are present, so the description must cover behavioral traits. It indicates the tool accesses review scores, decisions, and ratings, but does not mention safety (e.g., read-only nature), caching behavior (though a use_cache parameter exists), rate limits, or pagination. The description is adequate but not thorough.

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 a single, clear sentence that immediately conveys the tool's purpose. It is concise with no unnecessary words, effectively front-loading the key information.

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 has no output schema and 5 parameters fully described in the schema, the description provides adequate high-level context about what the tool returns (review data). However, it lacks details on error handling, date ranges, or prerequisites, but the overall complexity is low, so the description is mostly complete.

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 each parameter has a description. The tool description adds context beyond the schema by mentioning peer review scores, decisions, and ratings, which helps infer the meaning of parameters like decision. This extra context merits a score above the baseline of 3.

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 retrieves papers from OpenReview ML conferences, naming specific conferences (ICLR, NeurIPS, ICML). It distinguishes itself from sibling tools like get_arxiv_papers by specifying the source and type of data (peer review scores, decisions, ratings).

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 implies usage for accessing OpenReview paper data but does not explicitly state when to use this tool versus alternatives like get_arxiv_papers or get_paperswithcode_latest. No clear scenario-based guidance or exclusions are provided.

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