list_open_source_relevant_grants

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only implies a read-like action ('Discover') but does not explain any behavioral traits such as filtering behavior, data sources, rate limits, or whether it covers both open-source AI and public goods as separate categories. This is a minimal hint rather than a transparent description.

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, front-loaded sentence with zero filler. Every word contributes meaning, and it is appropriately concise for the tool's simplicity, even though other dimensions suffer from the brevity.

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?

While an output schema exists (so return values need no description), the tool has two parameters and no annotations, and the description lacks any context about the default focus value, how relevance is determined, or how this tool differs from sibling grant-listing tools. The description is too sparse to be considered complete for effective tool selection.

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 0%, so the description must compensate by explaining parameter meanings or usage, but it does not mention 'focus' or 'max_results' at all. It adds no value beyond the bare names and defaults in the schema, leaving the agent to guess what 'focus' means or how 'max_results' affects results.

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 "Discover grants particularly relevant to open-source AI and public goods" clearly states the tool's function (discover/list grants) and its scope (open-source AI and public goods). It does not explicitly distinguish from sibling tools like list_public_goods_funding, but the name and description provide a specific, actionable purpose.

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 no guidance on when to use this tool versus alternatives such as search_grants or list_public_goods_funding. It implies a discovery use case but does not state any conditions, exclusions, or context that would help an agent choose this tool over its siblings.

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