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

With no annotations, the description carries the burden. It discloses that matching is case-insensitive, which adds behavioral nuance beyond the schema. However, it does not state whether one or many rows are returned, what happens on no match, or what the response contains.

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 compact sentence with no filler. It front-loads the resource and states the filtering rule efficiently, though it lacks an explicit verb like 'returns' or 'fetches'.

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 core lookup semantics are clear, but the title says 'a row' while the description says 'rows,' creating ambiguity about cardinality. With no output schema and no annotations, the description should clarify return behavior and error cases; it does not.

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. It loosely maps column and value to the equality condition, but does not define whether column must be a key column, whether value supports patterns, or what constraints exist beyond minLength. For two simple params, the mapping is adequate but not detailed.

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 clearly states that the tool retrieves rows from the Funnelvo dataset based on an exact column-value match, and the title adds the 'look up by key' framing. It does not explicitly differentiate from siblings like dataset_search, but the meaning is specific enough for an agent to grasp the core function.

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?

No guidance is provided on when to use this tool versus alternatives such as dataset_search or dataset_compare. There are no exclusions, prerequisites, or hints about which tool suits fuzzy vs. exact lookups.

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