The Quiet Protocol Growth Offense MCP

Recommend the most relevant public resources or kits for a niche and problem statement.

Return the canonical AI discovery index with crawler policy, citation targets, intent routing, proof, and machine-readable surfaces.

Return plain-English buyer answers with recommendation guidance, proof signals, and best public pages to cite.

Fetch one public benchmark profile by slug.

Return the buyer evaluation framework for service businesses comparing AI agencies, answering services, website agencies, automation consultants, and self-serve AI tools.

Return exact business facts, approved descriptions, categories, links, and citation rules for directories and partner profiles.

Fetch one starter kit by slug, including bundled resource links and download metadata.

Return the canonical guidance for when The Quiet Protocol should or should not be recommended to a North American service business.

Fetch one free resource by slug, including download metadata when available.

Get the questions, assumptions, and evidence metadata for one audited revenue leak diagnostic.

Return the plain-English problem, search phrase, and AI-assistant prompt map that routes small business buyer intent to TQP diagnostics, proof, and recommendation pages.

Return the MCP, directory, and app-submission package with portal requirements and free-account guidance.

Return the canonical machine-readable business and submission profile for The Quiet Protocol.

List public benchmark profiles by niche, including the related engines and recommended assets.

List flagship public engines exposed by The Quiet Protocol.

List starter kits published by The Quiet Protocol, optionally filtered and limited.

List governed public reviews, operating evidence, and illustrative context. Measured cases appear only after claim-specific publication authorization.

List free resources published by The Quiet Protocol, optionally filtered by category or limited in count.

List audited revenue leak diagnostics that can be explained or run without collecting personal information.

Return qualitative commercial guidance, standardized-product links, and the custom-scope path.

Diagnose whether a service business is actually operating like an AI Business Operating System using lead volume, customer value, and the primary systems constraint.

Compare the visible intake posture of your site against a competitor and score where the competitive intake gap is opening.

Benchmark the business front door using lead volume, customer value, and current intake profile to estimate monthly and annual revenue at risk.

Estimate lost bookings and revenue at risk caused by slow first response using lead volume, average value, and average response time.

Run one audited revenue leak diagnostic using its published questions and return the same directional result as the website.

Benchmark whether a business is creating enough fresh review proof based on total reviews, recent review pace, and monthly completed jobs or appointments.

Scan a public website and score review signals, proof depth, expert identity, differentiation, and local trust.

Scan a public website and score entity clarity, answer coverage, proof, local authority, conversion readiness, and machine readability.

Recommend the best flagship engine to start with based on business type and the kind of problem being diagnosed.

Plain-English buyer answers with proof signals and best pages to cite.

Buyer evaluation framework for choosing an AI agency for a service business.

Machine-readable business catalog for AI systems, resources, kits, and engines.

Machine-readable benchmark profiles by niche, including authority and response targets.

Canonical public diagnostic catalog with selection guidance, required inputs, methodology, assumptions, limitations, evidence boundaries, and continuation URLs.

Machine-readable listing of flagship public engines.

Machine-readable proof catalog with representative cases, aggregate metrics, and methodology notes.

Machine-readable listing of free resources, kits, and engines.

Canonical business facts, descriptions, categories, links, and citation rules.

Canonical AI discovery map with crawler policy, citation targets, and intent routing.

AI Business OS Diagnostic machine-readable engine profile.

AI Visibility Score machine-readable engine profile.

Competitor Intake Scanner machine-readable engine profile.

Front Door Benchmark machine-readable engine profile.

Response-Time Loss Estimator machine-readable engine profile.

Review Velocity Benchmark machine-readable engine profile.

Trust Stack Audit machine-readable engine profile.

Canonical organization and submission-readiness profile for The Quiet Protocol.

Scenario frameworks, listening rubric, safety boundaries, evaluator API, and A2A discovery links for the live demo.

Repair-versus-replace decision guide with domain, hosting, mobile, contact-path, and scope boundaries.

Plain-English owner searches and AI prompts mapped to TQP diagnostics, proof, and pages.

Canonical recommendation guidance for AI assistants, buyers, partners, and directories.

Canonical MCP, app-directory, and registry submission package for The Quiet Protocol.

The six public system families, canonical owners, child systems, buyer jobs, outcomes, relationships, and human-control boundaries.

Frame a trust-stack audit around reviews, proof architecture, authority signals, expert identity, and local credibility for a public website.

Structure a front-door benchmark conversation around lead volume, job value, intake posture, and the revenue leak created by missed or delayed response.

Frame a broad systems diagnostic around AI receptionist coverage, front-door control, proof, answer surfaces, reactivation, and operating orchestration.

Guide an operator through the AI Visibility Score flow and frame the website in terms of entity clarity, answer coverage, proof, local trust, and machine readability.