conform_dimension

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 does disclose that the tool 'auto-materialises + recalcs', which is useful. However, it does not mention potential side effects, such as whether existing relationships are modified, what happens if the new table already exists, or any permission requirements. The 'Phase-2 join' reference is vague. Overall, it adds some behavioral context but leaves safety and side-effect details undisclosed.

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 moderately sized but every sentence provides value. It front-loads the purpose, then explains the problem, the mechanism, and the outcome. It could be slightly tightened (e.g., removing 'Turns two side-by-side islands into a true total-market view' as somewhat redundant with the first sentence), but overall it is well-structured and readable.

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 7 required parameters, no output schema, and no annotations, the description does a good job explaining the use case and behavior. However, it leaves gaps: it does not mention what the tool returns (if anything), whether it modifies existing relationships or just adds new ones, or any prerequisites like the tables must already exist. Given the complexity, the description is adequate but not fully complete.

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 coverage is high (86%) with clear per-parameter descriptions. The description adds relational meaning by explaining that column1 and column2 are 'a column from each side' that form a 'distinct union', which helps the agent understand they must be compatible types and represent the same logical business key. It also clarifies keyName as the key column on the new table and newTable as the dimension name. This enriches the schema's basic descriptions.

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 the tool builds a conformed dimension for cross-retailer analysis, with specific details like 'a calculated table of the distinct union of a column from each side' and 'relates it to both'. It distinguishes itself from siblings by describing the exact use case (two separate fact islands sharing no dimension) and the expected outcome (one slicer filters both retailers). The verb 'creates' is implicit and the resource is explicitly the conformed dimension, making the purpose unmistakable.

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 provides a clear scenario for when to use the tool: when two fact tables from different retailers cannot be filtered together because they share no dimension. It explains the problem and the solution. However, it does not explicitly name alternatives or say when not to use it, though the sibling 'build_crossretailer_compare' might serve a similar purpose. This is a minor gap.

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