Connect AI visibility audits to the systems your agency already runs.

MCP and API automation is the advanced layer of the VectorGap agency operating system. Use it after the audit has exposed brand risk, competitor preference, remediation priorities, and client-ready next actions: sync the evidence, create missions, request retests, and move white-label reporting into your agency workflow without losing the proof trail.

Get audit + 5 client credits Compare Agency OS plans

Use the 5-credit pre-sales kit flexibly, or spend the full pack on one complete audit before automating a client portfolio.
Read Perception, Preference, Web, GEO, competitor, analytics, mission, and Intelligence evidence with target context preserved.
Create actions, attach artifacts, update recommendations, complete missions, and request comparable retests.
Export Markdown, CSV, and PDF-backed reports while preserving brand, provider, market, language, persona, and competitor context.

standard providers

LLM prompts

Preference prompts

0-100

Generative Brand Index

First step

Audit your own agency first

The pre-sales kit gives your agency 5 flexible credits. Spend them à la carte, or use the full pack for one complete Presence, Perception, Preference, and AI Readiness audit before scaling automation.

Automation job

Evidence into operations

MCP and API workflows help advanced teams retrieve audits, create actions, attach proof, request retests, and export client-ready reports.

Agency fit

Agency OS and Enterprise layer

Automation is strongest when the agency is managing multiple brand brains, recurring missions, and repeatable reporting across a client portfolio.

What can agencies automate with the VectorGap MCP and API?

Agencies can automate the operating loop around AI visibility and brand governance: retrieve audit snapshots, inspect provider-level rows, compare competitors, export reports, create external action records, attach proof artifacts, update missions, request retests, and keep client reporting systems synchronized. The point is not to hide the evidence. The point is to move the evidence into the systems where strategy, client services, and delivery already work.

Read audit evidence

Pull Presence, Perception, Preference, AI Readiness, competitor, analytics, recommendation, mission, and Intelligence snapshots with the context needed for reporting. Provider, market, language, persona, competitor, source, and completed-audit context stay attached.

Workspace-safe snapshots
Provider-level rows
Score and evidence context

Create client work

Record external actions, submit source-strengthening work, attach proof URLs, update recommendation status, complete mission objectives, and request retests. Published pages, schema updates, partner mentions, and source wins remain connected to the original AI-answer gap.

External action evidence
Mission status updates
Comparable retest requests

Export white-label deliverables

Generate Markdown, CSV, and PDF-backed outputs for client reporting, internal QA, leadership summaries, and recurring retainer workflows. Exports should stay client-ready: readable executive summary, evidence context, mission progress, retest movement, and next actions.

Markdown reports
CSV evidence exports
PDF-backed summaries

How does automation support a recurring AI governance service?

A recurring AI visibility service needs repeatable evidence, not more manual screenshots. MCP and API workflows let the agency pull the current state, detect open gaps, record shipped work, retest comparable targets, and regenerate the report. That creates the rhythm a retainer needs: audit, evidence review, mission queue, implementation, retest, report, and next scope.

Portfolio reporting

Agencies managing many brands can pull current snapshots and report status without opening every workspace manually. The useful automation is knowing which clients have open source gaps, weak preference categories, overdue missions, hallucination risk, or completed retests.

Proof-loop continuity

When a team updates a website, publishes a source page, earns a mention, or completes a mission, automation can attach that artifact to the relevant gap. The next audit then has a clearer before-and-after trail for the client report.

Client-safe delivery

Automation should reduce production time while preserving the report structure clients can trust: executive summary, answer excerpts, source gaps, competitor pressure, completed work, retest movement, and next actions.

Where does MCP fit in the 2026 Agency OS offer?

MCP is not the broad hero promise. It is the automation layer for teams that have already proved the audit workflow and want to run it across a portfolio. Start with the free agency audit and 5 flexible credits, inspect the white-label action plan when you use the full pack, then move into Consultant, Agency OS, or Enterprise Governance depending on volume, reporting, and automation needs.

Prove the agency workflow first

Use 5 flexible credits à la carte, or spend the full pack on one complete Presence, Perception, Preference, and AI Readiness audit. Inspect where AI models describe you, prefer competitors, cite weak sources, or surface risky claims before selling the workflow to clients.

Open page

Choose the operating tier

Consultant is for controlled testing. Agency OS is the recommended plan for white-label reports, missions, MCP, and 250 credits. Enterprise Governance adds custom volume, API depth, and risk-sensitive support.

Open page

Scale the proof loop

Once the agency has a repeatable service line, automation keeps the evidence, actions, retests, and exports connected across the client portfolio.

Which evidence does AI need before it can reuse the page?

This table turns the page into a structured extraction target: the buyer question, the evidence an AI system can read, and the action an agency can sell or execute next.

Buyer question	What AI can extract	Agency action
Can we pull current audit evidence?	Completed LLM, Preference, Web, GEO, competitor, analytics, recommendation, mission, and Intelligence records with provider and target context.	Sync evidence into internal reports, dashboards, QA sheets, or client-service workflows.
Can we record work outside VectorGap?	External actions, artifact URLs, affected URLs, target gaps, expected metrics, and mission objectives.	Attach published proof and keep the retest narrative connected to the original gap.
Can we regenerate client deliverables?	Markdown, CSV, and PDF-backed report exports with source-grounded summary sections.	Produce recurring evidence packs without copying raw payloads into client decks.

Questions agencies ask before turning AI visibility into client work

Who should use the VectorGap MCP and API?

MCP and API workflows are for agencies with advanced reporting, automation, or internal operations needs. Most teams should prove the workflow first with the 5-credit audit, then use automation when they need recurring evidence, missions, retests, and exports across several client accounts.

Does automation replace the VectorGap dashboard?

No. The dashboard remains the main inspection surface for most teams. Automation moves approved evidence and actions into agency systems while preserving the source trail: provider, prompt, score, answer, source, competitor, market, language, persona, mission, and retest context.

What should agencies avoid when automating reports?

Do not send raw payload dumps or internal debug labels to clients. The report should stay structured and readable: executive summary, answer evidence, source gaps, competitor pressure, mission status, retest movement, and next actions.