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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that matching is exact and case-insensitive, which is real behavioral information beyond the schema. It says nothing about how many rows can come back, ordering, or pagination, which leaves meaningful gaps for a lookup tool.

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?

A single tight sentence, front-loaded with the resource and constrained by the match semantics. No filler. It is slightly under-formed as a sentence (no explicit verb), but nothing needs to be cut.

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 exact-match lookup with no annotations and no output schema, the description covers the core contract but omits return shape (row count, ordering, whether multiple matches are returned). An agent could call it, but would be guessing about the result.

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 both required parameters ('column' and 'value') are undocumented in the schema itself. The description partially compensates by explaining that 'column' names a dataset column and 'value' is compared exactly and case-insensitively, but it never says how column identifiers are formatted or whether unknown columns error. Partial compensation only.

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 the resource (rows of the Calcul Brut en Net dataset) and the exact operation (column equals value, case-insensitive), so an agent knows it is an exact-key lookup. It implicitly contrasts with the fuzzy matching a sibling like dataset_search would do via the word 'exactly'. It stops short of an explicit verb and never names a sibling, so it is clear but not fully differentiated.

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 statement of when to use this tool versus dataset_search, dataset_compare, or dataset_top. The only guidance is the implicit 'exact match' cue embedded in the purpose sentence. No prerequisites or exclusions are given.

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