SEO audits in Claude + Cursor: our new MCP server

SEO Score API has released an official Model Context Protocol (MCP) server that gives AI tools like Claude Desktop, Claude Code, Cursor, and Windsurf direct access to nine SEO tools, including audit, batch audit, backlinks, and history. The server enables AI to perform grounded SEO audits by calling the scoring engine directly, replacing generic advice with verifiable, real-time data. The backlinks tool surfaces a growing dataset of observed backlinks from audit pages, offering a unique feature at competitive pricing.

We just shipped an official Model Context Protocol server https://github.com/avansledright/seoscoreapi.com/tree/main/sdks/mcp . Install it once and Claude Desktop, Claude Code, Cursor, Windsurf, and any MCP-aware AI tool gets direct access to: audit , batch audit add monitor , list monitors history , history domains report url usage backlinks , Basic plan and up That's nine tools your AI can reach for the moment you ask "audit this URL" or "what backlinks point at my-startup.com?" Most AI tools today guess at SEO. The model has a fuzzy idea of what a good page looks like, an even fuzzier idea of how Google ranks anything in 2026, and zero idea what your specific page actually scores. So you get advice like "make sure your meta description is between 150 and 160 characters" — true, generic, and unverifiable. MCP changes the shape of that conversation. The AI doesn't need to guess; it can call our scoring engine directly. The same 82-check audit your CI pipeline runs is now one tool call away from any prompt. The difference in practice: Before MCP "Hey Claude, can you audit my site?" "I can't access URLs directly, but here are some general tips: make sure your title tags are 50-60 characters, write descriptive meta descriptions..." After MCP "Hey Claude, can you audit my site?" Claude calls the audit tool against https://seoscoreapi.com . "Your site scores 97.9 / A+. The only category with a flag is meta — your homepage description is 154 chars, well within range. AI readability scores 96/A+. The top priority would be... actually you have zero priorities right now. You're clean." That's not a parlor trick. That's grounded answer-engine output, and it's exactly the kind of answer LLMs are starting to be measured on. npx -y seoscoreapi-mcp Or, in your AI client's config: { "mcpServers": { "seoscoreapi": { "command": "npx", "args": "-y", "seoscoreapi-mcp" , "env": { "SEO SCORE API KEY": "ssa your key here" } } } } Get a free API key. https://dev.to/ signup The free tier covers 2 audits per day with no card required — enough to wire up the MCP server and run your first batch. Full setup instructions for Claude Desktop, Claude Code, Cursor, and Windsurf https://github.com/avansledright/seoscoreapi.com/tree/main/sdks/mcp are in the README. Real prompts that route through the MCP tools without you specifying which one: "Audit https://stripe.com and tell me the top 3 things to fix.""Compare the AI readability scores of stripe.com, square.com, and adyen.com." "What backlinks have we observed pointing at my-startup.com?" "Show me the score history for our marketing site over the last 30 days." "Are any of my monitored URLs trending down?" The AI picks the right tool. You don't. backlinks tool deserves its own paragraph This is unique to SEO Score API. Every audit our customers run contributes to a growing backlink graph: external links observed on each audited page get persisted into a public dataset. The backlinks tool surfaces that dataset for any domain, with an explicit data caveat in every response. It is not a comprehensive backlink index — Ahrefs crawls 8 billion pages a day and we don't, and we say so. It is honest, audit-fed, and grows with usage. For "show me referring pages we have actually seen," it works exactly the way you'd want. What backlinks does SEO Score API see pointing to stripe.com? via backlinks tool stripe.com: total observations: 142 referring domains: 38 top referring: docs.stripe.com 24 , github.com 18 , ... data caveat: Backlinks observed during SEO Score API audits. Not exhaustive — coverage grows over time. No competitor with our pricing offers anything like this, because no competitor has the audit volume we do feeding the graph. Two reasons. One: every developer audience we care about is moving into MCP-aware tools. Claude Desktop has it, Claude Code has it, Cursor has it, Windsurf has it, the OpenAI agents framework has it. If our API isn't reachable from the place customers actually do their work in 2026, we're invisible — even when we're the right answer. Two: MCP is the cleanest possible packaging of the API. There's no SDK to learn, no auth dance to script, no batching to build. The user types a question, the AI picks the tool, the API answers. Friction approaches zero. That's the kind of leverage we want. We have official Python and Node SDKs. We have a GitHub Action for CI https://dev.to/blog/github-actions-seo-audit . We have an n8n community node https://dev.to/blog/n8n-seo-automation . The MCP server is the same idea — meet developers where they already are — pointed at AI clients instead of CI runners. seoscoreapi-mcp is MIT-licensed and open source. The whole server is one file you can read in five minutes: https://github.com/avansledright/seoscoreapi.com/tree/main/sdks/mcp https://github.com/avansledright/seoscoreapi.com/tree/main/sdks/mcp If you build something on top of it, send us the link. We'll feature interesting integrations on the blog. That is the entire onboarding.