find_similar_columns

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

readOnlyHint=true covers safety, and the description adds meaningful algorithmic detail: the composite scoring signals, union-find clustering, and the four cluster categories. The 'investigate, not merge' warning gives an agent useful nuance far beyond what an annotation can express.

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 dense but well-organized: core behavior first, then signals, clustering, use cases, and a heuristic warning. The only somewhat peripheral line is the 'Mirrors jcm's find_similar_symbols' cross-reference, but overall it is efficient.

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?

Since there is no output schema, the description compensates by naming the cluster categories and the fusion mechanism. It doesn't spell out the return shape, but an agent receives enough guidance to choose and invoke the tool 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?

Schema description coverage is 100%, so all four parameters are already documented in the input schema. The description reinforces the conceptual idea of composite scoring, but does not need to repeat parameter mechanics; the baseline of 3 applies.

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 identifies a specific purpose and use cases: finding duplicate columns across datasets, surfacing naming drift, and detecting the same conceptual column spread across multiple datasets. It clearly distinguishes this from related sibling tools like find_unused_columns by emphasizing cross-dataset similarity.

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 says 'Use to find...' and gives concrete scenarios, plus a caution that results are heuristic, so the agent knows the result is a starting point. It doesn't explicitly list when-not-to-use or alternatives, but the use-case framing and caveat are strong.

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