fuzzy_cluster_column

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

No annotations are provided, so the description carries the full burden. It discloses that it appends Table.AddFuzzyClusterColumn to the table's M query, which implies a model mutation, and explains the threshold parameter. However, it does not specify whether the original column is modified, how missing values are handled, or what the actual output type of the new column is. These gaps prevent a higher score.

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?

Two concise sentences: the first states the core purpose with a helpful example, the second states the underlying implementation and threshold. No wasted words, front-loaded with the most actionable information.

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 tool with 6 parameters, 4 required, and no output schema, the description is reasonably complete: it explains the transformation, the threshold, and implementation detail (M query). It does not cover partition behavior or return values, but the core usage is clear. The presence of richer schema descriptions for most parameters compensates for minor omissions.

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 describes column, newColumn, threshold, and partitionName (67% coverage). The description adds value by explaining threshold as a '0..1 similarity cut-off' and the purpose of the new column (canonical cluster representative), but most parameter meaning is already present in the schema. It does not compensate for the missing table/sessionId descriptions, but this is acceptable given the moderate coverage.

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?

Description states a specific verb ('add a column') with a specific resource ('canonical cluster representative of a text column'), includes a concrete example ('Acme' / 'acme inc' / 'ACME'), and clearly distinguishes from sibling tools like fuzzy_group or fuzzy_merge by focusing on column-level clustering rather than row grouping or table merging.

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 clearly conveys the use case: fuzzy clustering of near-identical text values into a canonical representative column. It does not explicitly name alternatives or state when not to use it, but the context is unambiguous enough that an agent can determine when to invoke it. Missing explicit exclusions or comparisons to fuzzy_group/fuzzy_merge keeps it just below a 5.

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