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

With no annotations, the description carries the burden and does add useful behavior: case-insensitive substring matching in any cell and a 50-row cap. However, it does not disclose ordering, default behavior when limit is omitted, pagination, or what happens on zero matches, so it is only partially transparent.

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?

One sentence covers resource, matching behavior, case handling, and result limit without filler. The most important information is front-loaded.

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 two-parameter search tool, the description gives the core contract, but there is no output schema, no usage guidance, and no stated default for the optional limit. An agent can invoke it, but may not know whether one result set or paging is expected or which sibling to choose in ambiguous cases.

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 50%; the query property has a description, but limit does not. The description adds case-insensitivity and the 50-row cap, which clarifies result behavior, but it does not explain the limit parameter's default or semantics beyond the schema's min/max.

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 names a concrete resource (the Lobbyvo dataset) and a precise behavior: returning rows whose cells contain the query, case-insensitively, up to 50. This is specific enough that an agent can distinguish it from siblings like dataset_columns or dataset_stats, even though no sibling is named.

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?

There is no guidance on when to prefer this tool over alternatives. The description implies a text-search use case but never states exclusions, alternatives, or conditions such as 'use dataset_row for a single row'.

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