list_datasets

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

With no annotations provided, the description carries the full burden. It discloses that no API key is required (useful auth context) and describes the return content (dataset names, titles, summaries, entity counts). However, it doesn't mention behavioral aspects like rate limits, pagination, error conditions, or whether this is a read-only operation (though implied by 'List').

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 efficiently structured: first sentence states purpose and key constraint, second describes returns, third explains parameter usage, and fourth provides concrete examples. Every sentence adds value, with no redundant information, and it's front-loaded with the core functionality.

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 low complexity (single optional parameter, no output schema, no annotations), the description is largely complete. It covers purpose, usage, returns, and examples. The main gap is lack of output structure details (e.g., format of returned list), but since there's no output schema, this could be more explicitly addressed.

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 schema already fully documents the single optional 'query' parameter. The description adds minimal value beyond the schema by mentioning 'filter datasets by name, title, or summary' and providing example queries ('ofac', 'eu', 'pep'), but doesn't explain semantics like partial vs. exact matching. Baseline 3 is appropriate given high schema coverage.

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 specific action ('List') and resource ('available sanctions and PEP datasets in OpenSanctions'), distinguishing it from siblings like get_dataset (retrieve specific dataset) or search_entities (search within datasets). It explicitly mentions what information is returned (dataset names, titles, summaries, entity counts).

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 provides clear context for when to use this tool ('No API key required' indicates accessibility, and 'Use the optional query parameter to filter' suggests it's for browsing/filtering datasets). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the siblings (e.g., use get_dataset for detailed metadata of a specific dataset).

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