df_get_fact_table

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond that: it states what the fact table object contains and explains that include_dependencies=true returns the formula dependency tree for each measure. No annotation contradiction exists.

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 focused sentences with no filler. The core action and returned components are front-loaded, and the optional dependency behavior is added in a clear second sentence. Every clause 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?

Given the read-only annotations, required IDs documented in the schema, and the description's enumeration of returned content plus the dependency-tree option, the tool is adequately described for a single-object GET. The main omissions are explicit guidance on language/use_cache behavior and output formatting, but these are minor because no output schema exists and defaults are present.

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 only 33%, so the description must compensate, and it does for include_dependencies by explaining its effect. However, it leaves language and use_cache semantically unexplained, and fact_table_id has no description either. It adds some value beyond the schema but does not fully cover the gaps.

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 uses a specific verb and resource ('Get one fact table') and enumerates the contents returned: measures, dimensions, facts, dimension groups, and verification filters. This clearly distinguishes it from sibling tools like df_list_fact_tables, which list fact tables rather than retrieving one.

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 intended use is implied by 'Get one fact table' and the include_dependencies option, but there is no explicit when-to-use or when-not-to-use guidance and no named alternative. An agent can infer this is for detailed retrieval of a single fact table, but the description does not spell out the choice versus df_list_fact_tables or other read tools.

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