geo-seo-analyzer-octoboost
LocalAbout geo-seo-analyzer-octoboost
OctoBoost SEO MCP Server connects agents and MCP clients to OctoBoost audits, returning structured SEO and AI-visibility results instead of raw page content. It supports URL discovery, configurable analyzers, and progress updates during multi-URL audits.
Key features
- 01Discover analyzers and their categories
- 02Crawl domains for relevant URLs
- 03Audit one or more URLs
- 04Return SEO and GEO/AEO scores
- 05Report per-URL progress updates
- 06Honor robots.txt during scans
Use cases
- 01Audit a site's SEO and AI-search visibility
- 02Find relevant pages before a site audit
- 03Review scores across multiple URLs
MCP support
Exposes `list_analyzers`, `scan_domain`, and `analyze` tools to MCP clients. The tools return structured audit results, including scores, diagnostics, credit usage, and progress notifications.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y octoboost-mcp-serverOCTOBOOST_API_KEYSecretYour OctoBoost API key for authenticating requests to the SEO APILocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
3Saved tool list · updated 3w ago
This tool takes no parameters.
Details
From the project READMEOctoBoost SEO MCP Server
Expose the OctoBoost SEO API as Model Context Protocol (MCP) tools so agents can audit websites with compact, structured results instead of fetching and parsing raw HTML.
Get your free API key at octo-boost.com. New accounts include free credits to try every tool.
What This Server Does
octoboost-mcp-server gives an MCP client three core capabilities:
- discover available analyzers with
list_analyzers - crawl a domain for relevant URLs with
scan_domain - run full audits with
analyze
It is built for agent workflows that need SEO and AI-visibility signals inside the reasoning loop without spending thousands of tokens on raw page content.
Who It's For
This server is a good fit for:
- developers building MCP-enabled products, assistants, or internal automation
- teams using MCP clients such as Cursor or Claude Desktop and wanting SEO tooling via config only
- AI agent workflows that need token-efficient site audits, progress updates, and structured outputs they can reason over
It is less useful if you want a general SEO learning guide or a raw HTML scraping tool. The main value here is compact audit output for automated workflows.
Why Use This Instead Of Raw Scraping?
Running SEO checks directly in an LLM context is expensive. OctoBoost moves the heavy lifting to the API and returns only the signals an agent needs to decide what to do next.
- Token-efficient: structured results instead of raw HTML
- LLM-friendly: scores, flags, and diagnostics instead of prose parsing
- Credit-aware: responses include credits used and credits remaining
- Predictable errors:
401for invalid or expired keys,402for exhausted credits
Quick Start
- Get an API key from octo-boost.com.
- Add the server to your MCP client config.
- Call
list_analyzersto verify the connection.
{
"mcpServers": {
"octoboost-seo": {
"command": "npx",
"args": ["-y", "octoboost-mcp-server"],
"env": {
"OCTOBOOST_API_KEY": "your-api-key"
}
}
}
}
Common config locations:
- Claude Desktop:
~/Library/Application Support/Claude/claude_desktop_config.json - Cursor: Cursor MCP settings
- OpenClaw:
~/.openclaw/mcp.json
Core Workflow
Most agent flows follow this pattern:
1. list_analyzers
-> learn categories and available checks
2. scan_domain { domain: "acme.com" }
-> collect relevant URLs for the audit
3. analyze { urls: [...] }
-> run a full audit with SEO score + GEO/AEO score for each URL
Tools Overview
list_analyzers
Returns available analyzer keys, categories, and weights. Call this first so an agent knows what it can run.
- no input required
- current categories include
seo,accessibility,ux,performance, andgeo - weights returned reflect your personal setup (see Analysis Setup below)
scan_domain
Crawls a domain and returns SEO-relevant URLs.
| Parameter | Type | Default | Description |
|---|---|---|---|
domain | string | — | Domain or URL to scan |
maxPages | number | server default | Maximum pages to crawl (capped server-side at the batch analysis limit) |
excludePatterns | string[] | [] | URL patterns to skip |
respectRobotsTxt | boolean | true | Honor robots.txt |
defaultLanguageOnly | boolean | true | Skip alternate-language duplicates |
analyze
Runs a full audit for one or more URLs. All 30+ analyzers run on each URL, returning an overall SEO score, per-category scores, and a GEO/AEO score for AI search visibility. URLs are processed sequentially and emit progress notifications after each one.
| Parameter | Type | Description |
|---|---|---|
urls | string[] | URLs to analyze |
Cost: 3 credits per URL.
Analysis Setup
From your dashboard at octo-boost.com/dashboard, you can configure how much each analyzer contributes to the overall score. Set a weight between 0 and 5 for any of the 30+ analyzers.
- Weight 0 — the analyzer still runs but is excluded from the overall score calculation
- Weight 1–5 — higher values give an analyzer more influence over the final score
- Changes apply immediately to all future API calls made with your key
list_analyzersalways returns your current weights, so agents can adapt their reasoning to your setup
Core Web Vitals (CrUX)
Full audits include real-user performance data (LCP, CLS, INP, FCP, TTFB) from the Chrome UX Report API at the p75 percentile — the speed 75% of actual Chrome users experienced or better. Google uses these as Search ranking signals.
If no data is returned, the result includes the exact reason (e.g. insufficient traffic, URL not in Google's dataset). This is common for low-traffic sites and does not affect the overall SEO score.
GEO/AEO Output
Full audits include a geoScore alongside the technical SEO score.
This score is meant for AI-search and agent workflows. It helps answer whether a page is easy for systems like ChatGPT, Claude, Gemini, or Perplexity to understand, extract, retrieve, and cite.
Key fields include:
geoScoretechnicalAccesscontentStructureentityClarityauthoritySignalscitationLikelihoodragReadinessllmAssessmentwhyThisMattersForAgents
Project Status
Planned next:
- LLM-based prioritization and condensation for more compact output
- higher-level tools such as
get_fix_plan,summarize_top_opportunitiesandcompare_urls - better site-level workflows built on top of crawl plus analysis
- dedicated interface and API documentation
Comments
New to MCP?
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.