craft-gtm-mcp

Scale Paid Check

scale_paid_check

Read-only

Helps you decide whether to scale paid now: it takes your customer list (signing date, segment, monthly fee and any stop or return dates) and your churn reasons, and returns, for each customer group, scale, run a capped test, or hold, with the counts behind it, the stop rule, payback, a first draft response to each churn reason and a churn interview guide.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "product": "<product>",
      "business_model": "<business_model>",
      "billing_term": "<billing_term>",
      "as_of": "<as_of>",
      "customers": "<customers>",
      "churn_reasons": "<churn_reasons>",
      "interview_persona": "<interview_persona>"
    },
    "name": "scale_paid_check"
  }
}

Input Schema

Name	Required	Description
`as_of`	Yes	The date the list was pulled (YYYY-MM-DD)
`metrics`	No	Optional: each metric with its name, value, whether it counts customers or revenue, and its period
`product`	Yes	Product in one line
`paid_cac`	No	Optional: paid-only CAC
`test_cap`	No	Optional: all-in budget for a capped test
`customers`	Yes	One row per customer (1 to 2000 items)
`stop_line`	No	Optional (default 0.80): share of a cohort that must still pay at month 12. 0.80 is Shashwat's example line for a $36,000 contract, not a benchmark.
`blended_cac`	No	Optional: blended CAC, shown for contrast only
`billing_term`	Yes	How customers are billed
`gross_margin`	No	Optional (0 to 1): gross margin needed for the money section
`churn_reasons`	Yes	Reasons in customers' words, comma or line separated
`business_model`	Yes	Business model, passed on to retention_playbook
`win_rate_floor`	No	Optional (0 to 1): a win rate the buyer is confident paid can reach
`win_rate_target`	No	Optional (0 to 1): the highest win rate the buyer would believe
`interview_persona`	Yes	Role to interview, for the interview kit
`payback_line_months`	No	Optional (default 12): the longest acceptable paid payback in months. 12 is Shashwat's example line for a $36,000 contract, not a benchmark.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 8, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description goes further by disclosing the shape of the output — per-group verdicts, supporting counts, stop rule, payback, draft responses to each churn reason, and an interview guide — which tells the agent this is an analytical recommendation tool rather than a data mutation.

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

Conciseness4/5

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

The purpose is front-loaded in the first clause, followed by an input summary and output inventory. It is a single dense sentence, but for a 16-parameter tool with no output schema, nearly every clause carries useful information rather than restating the name.

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?

With 16 parameters and no output schema, the description correctly takes on the burden of explaining what comes back (verdicts, counts, stop rule, payback, drafts, interview guide). It does not explain how optional inputs like paid_cac, test_cap, or win-rate floors change the result, which is the main remaining gap.

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?

Schema description coverage is 100%, so the schema already documents all 16 parameters in detail, including defaults and enum meanings. The description only summarizes the customer-list fields and churn reasons at a high level, adding little meaning beyond what the schema provides; the baseline of 3 applies.

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 states a specific verb and decision ('Helps you decide whether to scale paid now') and a concrete resource (customer list plus churn reasons), then enumerates the per-group verdicts (scale, capped test, hold). This is clearly distinct from siblings such as retention_playbook or pmf_scorecard.

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?

It establishes the decision context (evaluating whether to increase paid spend) and names the inputs the agent must have on hand (customer list with signing/segment/fee/stop dates and churn reasons). It stops short of stating when NOT to use it or explicitly naming which sibling to pick instead, so it is clear context without exclusions.

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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