New: works over MCP

Audit any site from your AI assistant

CrawlRaven works as an MCP server inside Claude, ChatGPT, and Cursor. Run a 200-point technical SEO audit, pull a prioritized fix list, and get fix guidance, right in the chat.

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Works with any MCP client200-point auditNo dashboard required
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CrawlRaven exposes an MCP server so AI agents can run technical SEO audits as tool calls. Your agent can list projects, trigger a 200-point crawl, wait for results, and fetch issues already ranked by ranking impact, then turn them into fixes, tickets, or reports. It works with Claude, Cursor, Windsurf, and any MCP-compatible agent.

What your AI agent can do with CrawlRaven

The MCP server turns CrawlRaven's audit engine into tools your agent can call on its own, so SEO work happens where you already work.

Run audits from your agent

Ask your AI agent to audit a site in plain language. It calls CrawlRaven's MCP tools to kick off a 200-point crawl and waits for the results: no tab-switching.

Pull a prioritized fix list

Your agent fetches issues already ranked by ranking impact, so it can reason over what to fix first instead of drowning in a flat list of warnings.

Monitor projects on a loop

List projects, re-run audits, and compare runs over time. Wire CrawlRaven into an agent loop that watches for regressions and flags them as they appear.

Generate reports in context

Have your agent turn raw audit data into a client-ready summary, a ticket backlog, or a remediation plan, right inside the chat or workflow it already lives in.

Use cases

What you can do with CrawlRaven in your agent

Every CrawlRaven MCP tool is one message away. Here are the workflows teams run most — just type the prompt.

Audit a site on demand

Kick off a full 200-point crawl from chat and get the scored result back in the same thread — no dashboard.

Audit https://example.com, wait for it to finish, then give me the health score.
→ calls audit_run_and_wait

Pull the prioritized fix list

Fetch issues already ranked by impact so your agent reasons over what to fix first instead of a flat list.

List the top 10 issues from the latest audit, ranked by ranking impact.
→ calls issues_list_for_audit

Review past audits

Look back over previous runs and have your agent summarize exactly what changed between them.

Show me the last 5 audits for my site and summarize what changed.
→ calls audit_list · audit_get

Monitor every project

See all connected projects at a glance and spot the ones that need attention first.

Which of my projects has the most open critical issues right now?
→ calls project_list

Turn issues into a fix plan

Group ranked issues into a developer-ready remediation plan or a ticket backlog, grouped by priority.

Group the open issues into a remediation plan I can hand to a developer.
→ calls issues_list_for_audit

Catch regressions after a deploy

Re-run an audit in CI or after a release and flag anything that regressed since the last run.

Re-run the audit and tell me if anything regressed since last week.
→ calls audit_run
Connect with OAuth

Connect CrawlRaven to your agent

Point any OAuth-capable MCP client at the remote server, or call the same audit data over the REST API.

OAuth · no manual token required

Add the remote MCP URL in an OAuth-capable connector. The connector discovers CrawlRaven's auth metadata and prompts you to sign in before sending tool requests.

Connect Claude

  1. 1Open Claude settings and go to Connectors.
  2. 2Add a custom connector named CrawlRaven.
  3. 3Paste the endpoint URL below and complete the CrawlRaven sign-in prompt.
Endpoint URL
https://mcp.crawlraven.com
Advanced OAuth metadata URLs

Most users only need the endpoint URL above. These are for clients that inspect OAuth discovery directly.

Protected resource metadata
https://mcp.crawlraven.com/.well-known/oauth-protected-resource
Authorization server metadata
https://app.crawlraven.com/.well-known/oauth-authorization-server

Why run an audit from an agent instead of a dashboard

A dashboard is good at showing you everything. An agent is good at answering one question and then acting on the answer. Most SEO work is the second kind, which is why the exports and copy-paste steps in a normal audit take longer than the analysis.

The agent reads structured data, not a screenshot

This is the part people underestimate. Pasting a URL into a chat model does not crawl your site, and pasting a screenshot gives it a picture of a table. Over MCP, the model receives the actual audit results as data it can filter, sort, and reason about.

  • No export step. The crawl results arrive in the conversation, so there is no CSV round trip between finding an issue and working on it.
  • Follow-up questions stay cheap. Asking which of these are on pages that get traffic is one more message, not a second pass through a spreadsheet.
  • The output is drafts, not just findings. The same context that identified a schema problem can write the corrected markup.

What to hand the agent, and what to keep

The division that works is simple: the agent reads, sorts, and drafts, and you decide and verify. Anything it writes that ships to production is your call, not its.

  • Good agent work: summarising a crawl, grouping issues by template, ranking by likely impact, drafting schema, redirect rules, and robots directives.
  • Keep for yourself: deciding what actually gets fixed, judging business impact, and approving anything that changes indexing.
  • Always verify: run drafted structured data through a validator and drafted redirects through a redirect checker before they go live.

The failure mode to plan for

A model asked to find problems will find problems, including ones that are not there. Treat its output as a shortlist to confirm rather than a task list to execute, and the workflow holds up. Skip that step and you will ship a confident fix for an issue you never had.

Getting from zero to a working agent audit

The setup is one-time. After that, running an audit is a sentence.

  1. 01

    Create a CrawlRaven account and a project

    The MCP server runs audits against your own projects, so the project is what the agent will be pointed at.

  2. 02

    Connect the MCP server to your client

    Add CrawlRaven to Claude, Cursor, or any MCP-compatible client. Authorization happens through a browser sign-in, so there are no tokens to copy or rotate.

  3. 03

    Ask for an audit in plain language

    Your agent discovers the available tools on its own. Asking it to run an audit on your domain is enough to trigger a crawl and pull the results back.

  4. 04

    Ask it to prioritize before you read anything

    Have it rank findings by impact against effort and explain the reasoning. A ranked shortlist is far more useful than a complete list.

  5. 05

    Have it draft the tedious fixes

    Structured data, redirect rules, and robots directives are exactly the kind of precise, repetitive output models are reliable at producing.

  6. 06

    Validate everything, then re-audit

    Confirm each fix with the appropriate validator, then run the audit again so you can see the change rather than assume it.

Works with any MCP-compatible agent

MCP is an open standard, so CrawlRaven plugs into the agents you already use, and any new one that adopts the spec.

Claude
Cursor
Windsurf
Cline
Openclaw
Hermes

…and any other MCP client.

AI SEO agent & MCP FAQ

An AI SEO agent is an AI assistant, like Claude, Cursor, or any MCP-compatible agent, that can run SEO tasks on your behalf by calling tools. With CrawlRaven's MCP server connected, your agent can trigger technical SEO audits, retrieve a prioritized list of issues, and turn the results into reports or fixes without you opening a dashboard.
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Give your agent SEO superpowers

Sign up free, connect the MCP server, and run your first audit from inside your agent.

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