Agent infrastructure

MCP SEO — Model Context Protocol
& AI Discoverability

The Model Context Protocol (MCP) is Anthropic's open standard for connecting AI agents to external tools and data sources. For SEO, MCP changes everything: instead of an agent reading your website and inferring your data, it can call your APIs directly. Brands that publish MCP-compatible endpoints move from passively mentioned to actively queried.

Use Surfaceable as an MCP tool

The protocol

What is the Model Context Protocol?

MCP is an open protocol released by Anthropic in late 2024 that standardises how AI applications connect to data sources and tools. Think of it as a universal adapter: any MCP host (Claude Desktop, Cursor, VS Code, custom workflows) can connect to any MCP server (a Surfaceable audit, a Shopify store, a database) using the same protocol.

The AI agent side

MCP hosts

Applications that run AI models and want to connect to external capabilities. Examples: Claude Desktop, Cursor, VS Code with Copilot, custom LLM applications, enterprise AI workflows.

The tool side

MCP servers

Applications that expose tools, resources, and prompts over the MCP protocol. Surfaceable runs an MCP server that exposes 16 SEO and AI visibility tools callable from any MCP host.

Individual capabilities

MCP tools

Individual callable functions exposed by an MCP server. A tool might be `run_seo_audit`, `get_visibility_score`, or `check_llms_txt`. The AI agent decides which tools to call based on the task.

Why MCP is the plumbing of agentic SEO

Before MCP, AI agents interacting with your brand had to scrape HTML, parse unstructured text, and guess at your data. MCP enables a direct, structured, real-time connection. An agent helping a user evaluate SEO tools can now query Surfaceable's MCP server directly — getting authoritative, up-to-date data rather than outdated scraped content. For any brand that wants to be the authoritative source of truth for an AI agent, MCP is the mechanism.

Surfaceable MCP

Surfaceable as an MCP server

Every SEO and AI visibility tool in Surfaceable is exposed as an MCP tool. Add Surfaceable to Claude Desktop or any MCP host and you can run full SEO audits, track AI visibility, and benchmark competitors entirely from within your agentic workflow.

Available MCP tools

run_seo_audit

Crawl a URL and return a full SEO health report covering meta tags, schema, Core Web Vitals, and 20+ checks.

get_ai_visibility_score

Return the current visibility score, presence rate, and share of voice for a tracked project.

check_llms_txt

Validate whether a domain has a compliant llms.txt and return its contents.

check_ai_crawlers

Check whether GPTBot, ClaudeBot, PerplexityBot, and Google-Extended are allowed or blocked in robots.txt.

validate_schema

Extract and validate all JSON-LD structured data from a URL.

run_prompt_test

Run a single prompt against a specified AI platform and return the response with brand mention analysis.

get_competitor_comparison

Return AI visibility share of voice across your brand and tracked competitors.

get_audit_history

Return historical audit scores for a project with trend data.

Connecting Surfaceable to Claude Desktop

claude_desktop_config.json

{
  "mcpServers": {
    "surfaceable": {
      "command": "npx",
      "args": [
        "-y",
        "@surfaceable/mcp-server"
      ],
      "env": {
        "SURFACEABLE_API_KEY": "your_api_key"
      }
    }
  }
}

After saving and restarting Claude Desktop, all Surfaceable MCP tools are available in every conversation. You can run SEO audits, check AI visibility scores, and validate structured data without leaving your workflow.

Full MCP setup guide

Use cases

How brands use MCP for SEO workflows

MCP unlocks SEO workflows that weren't possible with traditional tools. Here are the most common patterns used by Surfaceable customers today.

Automated audit on deploy

Trigger a Surfaceable SEO audit via MCP whenever a new page is deployed. Claude or a custom agent reviews the results and creates a GitHub issue if any critical checks fail — zero manual review.

Content brief generation

An agent runs `get_ai_visibility_score` for a query, identifies where you're invisible, then generates a content brief that targets the specific gaps. The brief includes structural recommendations derived from competitor pages that are being cited.

Weekly executive summary

A scheduled agent runs every Monday, calls `get_audit_history` and `get_competitor_comparison`, and writes a natural-language summary of SEO and AI visibility trends — delivered to Slack or email.

Schema validation pipeline

Every time a new blog post or product page is published, an agent calls `validate_schema` to confirm JSON-LD is correct. If schema is missing or malformed, it generates the fix and opens a pull request.

Competitor intelligence feed

An agent runs `run_prompt_test` across a set of competitive queries daily, tracking which brands appear in AI responses. Surfaces emerging competitor mentions before they show up in traditional share-of-voice reports.

Developer CLI integration

Engineering teams add `surfaceable audit {url}` to their CI/CD pipelines. Build fails if the SEO score drops below a configured threshold — treating SEO health as a first-class code quality concern.

The bigger picture

Agent-native SEO: what it means for your brand

Agent-native SEO is the idea that brands should be discoverable, queryable, and trustworthy not just to human searchers and search engine crawlers, but to autonomous AI agents completing tasks on behalf of users.

In the agentic web, discoverability has three layers:

Layer 1: Content

Your website content is structured, factually dense, and crawlable — so agents reading HTML can accurately represent you.

Layer 2: Metadata

You have llms.txt, valid JSON-LD schema, and correct robots.txt configuration — so agents can understand your site architecture without reading every page.

Layer 3: APIs (MCP)

You expose MCP-compatible endpoints — so agents can query your live data directly, bypassing static content entirely and getting authoritative real-time answers.

Surfaceable covers all three layers

SEO audit: 20+ content and technical checks
llms.txt detection and validation
JSON-LD schema validation
AI crawler access audit
Live AI visibility tracking (ChatGPT, Claude, Gemini, Perplexity)
MCP server with 16 tools for agentic workflows
CLI for CI/CD integration
REST API for custom dashboards

Get started free

Agentic SEO

The broader discipline of optimising your brand for autonomous AI agents and agent-driven search.

LLM SEO

How to structure your content and entity signals so large language models surface your brand.

Feature: AI Visibility Tracking

Measure your brand's presence across all major AI platforms — automated and scheduled.

Connect Surfaceable to your AI agent workflows.

MCP server, CLI, and REST API included on Growth and Pro plans.