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gf.revenue_intelligence

gf.revenue_intelligence

Read aggregate discovery, payment, confirmed revenue and repeat-buyer metrics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / additionalProperties
      Previous value: -falseNew value: +{}
    • removedInput schema / properties / days
      Removed value: -{
      -  "maximum": 365,
      -  "minimum": 1,
      -  "type": "integer"
      -}
  2. First observed

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. The word 'Read' does communicate a read-only, non-mutating intent, but the description adds nothing about data freshness, permissions, caching, aggregation windows, or response shape. It is minimally transparent but not rich in 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 a single, front-loaded sentence with no wasted words. It places the action ('Read') first and tucks the object and categories immediately after, making it easy for an agent to scan.

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?

For a zero-parameter, no-output-schema read tool, the description is mostly complete: an agent can call it immediately and the domain of 'revenue intelligence' is clear. It does not specify whether the metrics are global, date-bounded, or in a particular currency, but the low complexity makes this acceptable.

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 tool has no parameters, and the schema requires nothing, so the description does not need to add parameter detail. The 0-parameter baseline of 4 applies, and the description adds no contradictory or confusing parameter expectations.

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

Purpose4/5

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

The description states a specific verb ('Read') and resource ('aggregate discovery, payment, confirmed revenue and repeat-buyer metrics'), so the core action is clear. It does not explicitly compare itself to siblings such as payment_status or settlement_status, but the aggregate/revenue-measure focus gives reasonable differentiation.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool instead of similar status or qualifying tools. The description only says what it reads, not which scenario calls for it or which sibling alternatives should be preferred.

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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