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alexboissAV

artefact-revenue-intelligence

by alexboissAV

RFM Analysis

run_rfm
Read-onlyIdempotent

Segment clients by recency, frequency, and monetary value. Score purchase behavior, identify top-performing ICP patterns, and detect win/loss signals to prioritize revenue opportunities.

Instructions

Run RFM (Recency, Frequency, Monetary) analysis on client data.

Scores clients based on purchase behavior, segments them into 11 categories, extracts ICP patterns from top performers, and detects win/loss pattern signals.

Args: source: Data source — "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live HubSpot data, "sample" for built-in demo data. industry_preset: Scoring preset — "b2b_service", "saas", "manufacturing", or "default".

Returns: JSON with scored clients, segment distribution, ICP patterns, signals, and tier recommendations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoauto
industry_presetNodefault

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. Description adds that it returns JSON with scored clients and segments, but does not go beyond what annotations provide.

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?

Compact description with clear structure: purpose, capabilities, args, returns. Front-loaded with main action. Every sentence adds value.

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

Completeness5/5

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

With only two optional parameters and an output schema, the description covers functionality, parameter details, and return value sufficiently for an RFM analysis tool.

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

Parameters5/5

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

Schema has 0% description coverage, but the tool description provides detailed explanations for both 'source' and 'industry_preset' parameters, including valid values and defaults.

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?

Clearly states the tool runs RFM analysis, including specific steps like scoring, segmentation, and pattern detection. Differentiates from siblings like 'detect_signals' by focusing on RFM.

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?

No explicit guidance on when to use this tool versus alternatives. Description implies use for RFM analysis but does not mention exclusions or when to choose other tools like 'analyze_engine'.

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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