About MCP

Supercharge your AI assistant with professional-grade content analysis. The Markup AI MCP server connects MCP clients like Claude Desktop, Claude Code, and Cursor to Markup AI’s content agents, giving you brand-voice, clarity, persona, terminology, and AI-search checks directly in your AI-powered workflows.

What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI assistants call external tools and services. By connecting the Markup AI MCP server, you give your AI assistant the ability to:

• 🎯 Check brand voice — flag passages that have drifted from your approved voice
• 👤 Test against a persona — see how a target reader reacts to your content
• ✏️ Improve clarity — surface hard-to-read sentences with suggested rewrites
• 🤖 Detect AI-sounding copy — catch the giveaways of LLM-written text
• 📒 Enforce terminology — check content against your approved term list
• 🔍 Get found by AI search — check snippet readiness, sourcing, and freshness

Three tools, fourteen agents

The server exposes three tools. You describe what you want checked, and the review runs the right set of agents together in a single call.

An agent is a specialized content check—brand voice, persona reaction, sentence clarity, terminology, claims, and more. Each agent runs against the settings configured for your organization in the Markup AI console. You configure your brand voice, personas, terminology, and style guide once, and every call applies them automatically.

Setup

Connect to the hosted MCP server at https://api.markup.ai/mcp/ from any MCP-compatible client. Add the server URL to your client and authenticate.

You can authenticate in one of two ways:

• Log in with your Markup AI account (OAuth). When your client supports it, add the server and follow the browser prompt to authorize. Best for interactive use.
• Use an API key. Send your key in an Authorization header with the value Bearer YOUR_API_KEY. Best for headless or shared configurations.

Prerequisites

Before you begin, ensure you have:

• An MCP-compatible client (Claude Desktop, Claude Code, Cursor, or similar)
• A Markup AI account (sign up) — and an API key if your client authenticates with a header

Quick Setup Guide

MCP Examples

Explore client‑specific setup guides. Each page includes configuration, restart/verify steps, and troubleshooting.

The tools

markupai_review

Review content against your organization’s configured quality checks in one call. This is the tool your assistant should reach for whenever someone says “check this,” “review this,” or “clean this up before I publish.”

With no goal, the standard review set runs:

style_agent, brand_voice, terminology, sentence_clarity,
ai_voice_detector, generic_claims, source_authority

With a goal, Markup AI composes the check set for you. Instead of naming agents, describe the outcome—“get this ranking in AI search before launch,” “convince skeptical enterprise buyers,” “make sure legal won’t flag this”—and the server selects and orders the right agents for that goal, on top of the standard set. It also asks the agents that support it to focus their findings on your goal.

markupai_run_agents

Run an explicit, fixed set of agents. Use this when the user names specific checks, or when an integration needs the same agents every time.

Your organization’s configured profiles and guides apply unless you override them:

Get valid names and IDs from markupai_capabilities_and_configs.

markupai_capabilities_and_configs

Pull down everything your organization has configured, in one read-only call with no arguments:

• agents — name, one-line description, and category for every agent you can run
• personas — ID, name, and title
• voice_profiles — ID and name
• style_guides — ID and name
• content_profiles — ID and name

It returns a compact catalog—names and one-line descriptions, no JSON schemas. You rarely need it before markupai_review, which applies your configuration automatically; it’s most useful when you’re about to override something in markupai_run_agents.

Available agents

Every agent below can be named in markupai_run_agents, and any of them can be selected by markupai_review when your goal calls for it. Which ones your organization can actually run depends on your plan and feature access—check with markupai_capabilities_and_configs.

What you get back

Both execute tools return the same evidence envelope, so your assistant always knows what actually ran—not just what was found:

{
  "checked": {
    "agents_run": ["style_agent", "brand_voice", "terminology"],
    "selection": "default",
    "voice_profile": "Markup AI",
    "style_guide": "Technical",
    "persona": null,
    "characters": 1840
  },
  "warnings": [],
  "results": [
    {
      "agent": "brand_voice",
      "issues": [
        {
          "agent": "brand_voice",
          "severity": "medium",
          "confidence": 0.82,
          "explanation": "Hedged phrasing undercuts the confident voice.",
          "position": { "start": 412, "end": 448, "text": "we think this might help" },
          "category": "tone",
          "suggestion": "this helps"
        }
      ]
    }
  ],
  "duration_seconds": 12.4
}

Worth knowing:

• checked.selection tells you how the agent set was chosen: default (standard set), goal (composed from your goal), or explicit (you named them).
• checked.goal appears only when you passed a goal, and shows how it was interpreted—the agents it added and the resulting plan.
• Configuration evidence is honest. voice_profile, style_guide, and persona are populated only when an agent that consumes them actually ran.
• warnings are never stripped. An unconfigured voice profile, an agent your plan gates out, or a partial failure shows up here with a code and explanation—so an empty result is never mistaken for a clean bill of health.
• results has one entry per agent that completed, even when the agent found nothing. Agents that fail appear as agent_failed warnings instead.
• scores appear inside the style_agent entry when your organization has numeric scoring enabled.

Example prompts

Review before publishing

Review this launch announcement before I publish it: [paste text]

Review with a goal

Review this page with the goal of getting it cited by AI search assistants: [paste text]

Check this landing page copy—my goal is to convince skeptical enterprise buyers: [paste text]

Named checks only

Run just brand voice and claims on this paragraph: [paste text]

Check against a specific persona

Run the persona agent on this post as Casey, our Head of Technical Documentation: [paste text]

See what’s configured

What Markup AI agents, personas, and style guides do we have available?

Per-agent tool names

Some clients and saved configurations reference a tool name per agent. Those names map onto the three-tool surface like this:

Two related points:

• MCP addresses agents by catalog name. Pass an unknown name and the error lists every valid one. The REST API addresses agents by ag_ ID.
• MCP has no webhook_url parameter. The REST API supports webhooks.

Troubleshooting

Tips for Success

Learn More

• 📚 MCP Documentation — Understand the protocol powering this integration
• 🧠 Agent Catalog — What each agent checks and why it matters
• 🚀 Markup AI API Docs — Explore all API capabilities
• 🛠️ Markup AI Console — Configure your brand voice, personas, terminology, and style guide

Ready to transform your writing? Connect the Markup AI MCP server and bring AI-powered content checks into your assistant.