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add_visual_filter

Add a visual-level filter to exclude unwanted rows from tables and rankings, using comparison operators like gt, lte, or isnotblank to remove zero or blank values.

Instructions

Add a VISUAL-LEVEL filter to an existing visual. op = gt|gte|lt|lte|eq|ne|isblank|isnotblank. kind=column|measure. The classic use: exclude discontinued/zero rows from a ranking table, e.g. op=isnotblank on 'Sales 52W TY' (or op=gt value=0), so -100%/blank-rank SKUs stop cluttering the top. valueType=int|decimal|string (default decimal) for comparison values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opNogt|gte|lt|lte|eq|ne|isblank|isnotblankisnotblank
kindNocolumn|measuremeasure
fieldYesfield (column or measure) to filter on
tableYestable that owns the filter field
valueNocomparison value (ignored for isblank/isnotblank)
pageNameYes
valueTypeNoint|decimal|stringdecimal
visualNameYes
reportSessionIdYes
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing side effects. It clearly implies mutation ('Add') and describes the filter semantics in detail, but it does not explain whether the filter replaces or appends to existing filters, or any permission requirements. Thus it provides some value but lacks deep behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise yet information-dense, using two sentences to convey the core purpose, parameter formats, and a practical example. It is front-loaded with the main action and avoids fluff, every sentence earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the moderate complexity and lack of output schema, the description covers the essential usage with an example and parameter guidance. It does not mention response/return behavior or how to clear filters, but for a simple additive operation this is acceptable and the example anchors the practical context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description enriches the schema by explaining the meaning of 'op', 'kind', and 'valueType' with explicit allowed values and a realistic example. It also clarifies the role of 'field' and 'table' in context. Since schema coverage is 67%, the description compensates substantially for less-documented parameters, though a few parameters like reportSessionId remain unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool adds a visual-level filter to an existing visual, using specific verbs and resource context. It distinguishes from sibling tools like add_page_filter or add_report_filter by emphasizing 'VISUAL-LEVEL'. The enumeration of operators and kinds further clarifies exact functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a concrete classic use case (excluding discontinued/zero rows from a ranking table), which gives clear context on when to apply this tool. However, it does not explicitly mention when not to use it or point to alternative filter tools (e.g., add_between_filter for range filters), so it stops short of full comparative guidance.

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

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