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run_ai_audit

Run Agency Growth's free AI audit of the user's business. Ask the user for each field first; never guess or invent any of them, and only call this once the user has given their name and email and agreed to receive the result by email. Takes about a minute. Returns the three automations ranked by hours or money saved, and a link to the full result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe user's name.
emailYesThe user's email. The full result is sent here.
triedNoWhat they have already tried, optional.
budgetYesBudget to fix the problem: under-2k = Under $2K, 2k-5k = $2K to $5K, 5k-15k = $5K to $15K, 15k-50k = $15K to $50K, 50k-plus = $50K+.
problemYesTheir biggest operational problem, in their own words (at least a sentence).
revenueYesAnnual revenue band: under-500k = Under $500K, 500k-1m = $500K to $1M, 1m-2.5m = $1M to $2.5M, 2.5m-10m = $2.5M to $10M, 10m-plus = $10M+.
websiteNoBusiness website, optional.
businessYesBusiness name.
industryYesWhat the business does, e.g. "HVAC contractor".
hours_lostNoHours a week lost to the problem, optional.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Adds real behavior beyond the annotations: ~1 minute runtime (latency), the email consent requirement implying an email side effect, and the shape of the return. The annotations already signal openWorldHint=true and non-destructive, so the added runtime and consent context is meaningful. It could be stronger by stating that the data is submitted externally.

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?

Front-loaded with the action in the first sentence, then prerequisites, timing, and return value in short clauses. Every sentence earns its place, though the middle instruction is dense enough that it reads more as a checklist than prose.

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?

For a 10-param, 7-required, no-output-schema tool, the description supplies the missing return shape, timing, and consent preconditions. Nothing an agent needs in order to invoke it correctly is absent.

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 every field including the enum bands is already documented. The description only adds a global rule ("never guess or invent any of them") rather than per-parameter meaning. Baseline 3 is appropriate when the schema carries the semantics.

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?

States a specific verb+resource ("Run Agency Growth's free AI audit of the user's business"), naming the actor and the object. It is trivially distinguishable from the informational siblings about_agency_growth and list_plans, since this is an action tool. No ambiguity about what gets produced.

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

Usage Guidelines5/5

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

Explicit invocation gate: "only call this once the user has given their name and email and agreed to receive the result by email," plus "Ask the user for each field first; never guess or invent any of them." This tells the agent both when it may call and what it must collect beforehand — no alternatives need naming because the siblings do unrelated things.

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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