df_write_fact_table

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

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=true. The description reinforces the write nature and adds a non-obvious behavioral fact: fact tables created via the API lack a base physical table, which is valuable context. It does not contradict annotations and provides extra nuance without repeating the structured hints.

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 sentences, front-loaded with the core action and scoped by the fact-table resource. The second sentence adds a relevant behavioral caveat and points to a sibling tool. Zero fluff; every word earns its place.

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 description covers purpose and one behavioral nuance, but for a 7-parameter tool with no output schema, it leaves out critical invocation details like which parameters apply to which mode (especially fact_table_id for replace/update). The schema partially compensates for mode semantics, but the description is not fully complete for an agent to call all modes correctly on its own.

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 57%, with three parameters (name, description, fact_table_id) lacking schema descriptions. The tool description does not mention any parameter by name or explain how mode interacts with fact_table_id (e.g., that replace/update require an existing fact_table_id). It adds no meaning beyond the schema, leaving gaps that the schema alone does not fill.

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 opens with 'WRITES TO DATAFORGE. Create, replace or update a fact table,' which clearly identifies the verb (create/replace/update), resource (fact table), and scope. It distinguishes itself from sibling tools like df_write_fact and df_write_dimension by explicitly targeting fact tables, and adds a differentiating note about API-created tables having no base physical table.

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?

It clearly states the operation types and adds a practical pointer: 'attach elements with df_assign_to_fact_table.' However, it does not explicitly state when to choose this tool over sibling write tools (e.g., df_write_fact for rows, df_bulk_write_facts for bulk loads), nor does it state exclusions. The context is clear but not exhaustive.

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