df_assign_to_fact_table

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

Annotations already mark the call as a non-read mutation; the description adds genuinely useful behavior beyond that: ids are applied in order, and already-assigned or unknown ids yield a partial status via failed[] instead of failing the whole call. This clarifies failure semantics that neither the annotations nor the schema convey.

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 purposeful sentences with the operation warning front-loaded, followed by the action and the most important edge-case behavior. There is no filler or repetition of schema material.

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 note about failed[] and status=partial is helpful, but with no output schema the description still does not explain the success response shape or overall return contract. For a mutating tool with six parameters, that leaves an agent needing to infer the rest of the response behavior.

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 50%, and the description adds meaning for element_type, fact_table_id, and element_ids by naming the element kinds and stating that ids are applied in order. However, it leaves the project_id/version_id scoping and the exact form of element_ids ambiguous, so compensation for the schema gap is only partial.

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 a specific verb ('Attach'), a precise resource ('existing measures, dimensions, facts or dimension groups to a fact table'), and the leading 'WRITES TO DATAFORGE' signals a mutating operation. This separates it clearly from read-only siblings and from df_unassign_from_fact_table without needing to inspect schemas.

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 phrase 'Attach existing...' establishes the intended context: this is for linking already-created elements to a fact table, which implicitly distinguishes it from the df_write_* creators and the df_unassign sibling. It does not explicitly name alternatives or exclusions, so it stops short of a 5.

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