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

Annotations already cover the read-only, non-destructive, idempotent, and open-world profile. The description adds useful behavioral context beyond those annotations by clarifying that the tool searches public datasets and returns cited passages, which tells the agent what kind of evidence it can expect and that no private-data access is assumed.

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 efficient sentence that front-loads the core action and resource, then adds a purposeful rationale. Every clause earns its place, with no redundant or filler wording.

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?

The tool is simple, has an output schema, and annotations cover its safety profile. The description together with the schema covers what query to provide, the optional dataset filter, and the top_k bounds. It could be more complete by explicitly distinguishing itself from sibling list/propose tools, but nothing essential is missing for correct invocation.

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 coverage is 67%: query and dataset are described, but top_k is not. The tool description does not add parameter-level meaning beyond the schema, and it does not compensate for the undocumented top_k parameter. It slightly reinforces the dataset scope but otherwise leaves parameter semantics to the schema.

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 ('Look up'), a clear resource ('cited passages from public Koskamo datasets'), and an explicit purpose ('answer from facts instead of guessing'). This distinguishes it from sibling tools like list_datasets and propose_revision, which serve different operations.

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 gives a clear intended context: use this tool when the agent needs cited evidence to ground an answer. It does not explicitly name alternatives or when-not-to-use conditions, but the purpose clause effectively frames when retrieval is appropriate relative to the other revision/list tools.

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