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

With no annotations provided, the description carries the full behavioral burden. It adds useful context by disclosing case-insensitive substring matching and a 50-row cap, but it does not mention ordering, default limit behavior, empty result handling, or whether the search applies to all cell types.

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 sentence with no filler. It front-loads the result type and matching rule, and each clause earns its place by adding matching semantics, case sensitivity, and result cap.

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?

For a simple two-parameter search tool, the description covers the essential facts: what is searched, matching behavior, and result cap. Missing details like return order or output format are minor because the dataset structure is likely available through sibling tools and the tool is straightforward to invoke correctly.

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 schema already documents the 'query' parameter and provides min/max constraints for 'limit', so the description adds moderate value by stating case-insensitive matching and 'up to 50'. However, it does not explicitly explain that the 'limit' parameter controls the number of returned rows, leaving that inference to the agent.

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 specifies that the tool returns rows from the Batesio dataset when cells contain the query, clearly distinguishing it from sibling tools such as dataset_columns, dataset_stats, or dataset_provenance. The resource and matching behavior are stated explicitly, leaving little ambiguity about what the tool does.

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 the tool is used when you need rows matching arbitrary cell text, but it does not name sibling alternatives or state when not to use this tool. There is no explicit routing guidance such as 'use dataset_stats for aggregations' or 'use dataset_row for exact row access.'

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