pbi_set_conditional_format

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

The description goes beyond the minimal annotations (readOnly=false, destructive=false) by disclosing that an existing rule on the same target is replaced ('se sustituye') and that two gradients do not combine but overwrite. It also explains null_strategy variants. This adds meaningful behavioral context, though it doesn't cover all side effects like persistence or permissions.

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 concise and well-structured. It opens with a clear one-line summary, gives practical use-case examples, then uses a structured list for parameter explanations. Every sentence serves a purpose, and the formatting makes it easy to scan.

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 tool's complexity (9 params, 5 required), the description covers the core mechanics and key parameter semantics. The output schema exists, so return values needn't be described. It lacks a few details like acceptable color formats or an explicit note that the field must be numeric, but overall it's sufficiently complete for an agent to invoke the tool correctly.

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?

With 0% schema coverage, the description carries the burden of explaining parameters. It clearly explains field ('Tabla[Campo]' or '[Medida]'), target ('background', 'font', 'bars'), mid_color (three-stop gradient), and null_strategy (asZero | none | specificColor). Other parameters like page, visual_id, min_color, max_color are self-explanatory from names, but request_id is left undocumented. The description adds significant value for the most nuanced parameters.

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's function: 'Colorea un visual segun el valor de un campo (degradado).' It specifies the resource (visual), the action (color conditional formatting), and provides concrete examples (heat map, traffic light scale), distinguishing it from sibling formatting tools like pbi_set_visual_title.

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 gives clear context for when to use the tool: converting a matrix into a heat map or flat bars into a traffic-light scale. It also explains behavior (replaces existing rules) which helps usage, but it does not explicitly mention alternatives or when not to use it, so it misses the top score.

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