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ChurnLens

by io.github.kindrat86
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analyze_revenue_concentration

Assess customer concentration risk across a book of revenue using the Herfindahl-Hirschman Index (HHI), top-N share and whale detection. Answers the question every acquirer asks first: how much revenue walks out with one logo?

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoHow many top customers to aggregate for the top-N share. Default 5.
customer_mrrsYesPer-customer MRR values. Order does not matter.
whale_threshold_pctNoRevenue share above which a customer counts as a whale. Default 25.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / customer_mrrs / description
      Added value: +"Per-customer MRR values. Order does not matter."
    • addedInput schema / properties / top_n
      Added value: +{
+  "description": "How many top customers to aggregate for the top-N share. Default 5.",
+  "type": "number"
+}
    • addedInput schema / properties / whale_threshold_pct
      Added value: +{
+  "description": "Revenue share above which a customer counts as a whale. Default 25.",
+  "type": "number"
+}
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

The description names the metrics it computes (HHI, top-N share, whale detection) but does not disclose assumptions, computational complexity, or any limitations. With no annotations provided, more behavioral detail would be beneficial.

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?

Two sentences, each adding value. The first sentence defines the core metrics; the second gives a concrete use case. No redundancy or irrelevant information.

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

Completeness3/5

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

The description explains inputs and high-level computation but lacks details on output format or interpretation (e.g., HHI scale). Given no output schema, more return-value guidance would improve completeness.

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

Parameters3/5

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

The input schema already provides complete descriptions for all three parameters (100% coverage), including defaults. The description adds only minor context (e.g., 'Order does not matter' for customer_mrrs), so baseline score is appropriate.

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 identifies the tool's purpose: assessing customer concentration risk using specific metrics (HHI, top-N share, whale detection). It directly answers a concrete question, distinguishing it from sibling tools like churn rate or LTV calculations.

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 clear context for when to use the tool (acquisition due diligence) but does not explicitly mention when not to use it or suggest alternatives. Nonetheless, the context is sufficiently distinctive.

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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