geoforge

Open-source GEO/AEO (Generative and Answer Engine Optimization) auditor, llms.txt compiler and MCP server. Optimize websites and online stores for ChatGPT Search, Google AI Overviews, Claude and Perplexity. Runs 100% locally, no paid API.

Why

Naive llms.txt generators dump dirty HTML into one file. Commercial GEO platforms charge $100-500 per month and only point at problems. geoforge audits your site and compiles the files you actually deploy:

Related MCP server: ai-readiness

Architecture

crawler (robots, sitemap, fetcher)
   -> extractor (dom-cleaner, entity-graph, page-classifier)
      -> analyzer (token-counter, geo-scorer)
         -> compiler (llms-txt, schema-generator, report-generator)
CLI (commander)  and  MCP server (@modelcontextprotocol/sdk) share src/pipeline.ts

Usage

npm install && npm run build
node dist/cli.js audit https://your-shop.com --max-pages 25
node dist/cli.js build https://your-shop.com --out ./geoforge-output --max-full-tokens 80000
node dist/cli.js build ./my-static-site            # local HTML file or directory also works
node dist/cli.js build https://your-shop.com --allow-training

Options: --max-pages, --max-index-tokens (default 6000), --max-full-tokens (default 80000), --allow-training, --json (audit). During development use npx tsx src/cli.ts ....

MCP server

Claude Code:

claude mcp add geoforge -- node /absolute/path/to/geoforge/dist/cli.js mcp

Claude Desktop / Cursor (mcpServers config):

{ "mcpServers": { "geoforge": { "command": "node", "args": ["/absolute/path/to/geoforge/dist/cli.js", "mcp"] } } }

Tools: audit_site_geo, generate_geo_artifacts, analyze_robots_ai, extract_page_for_llm.

Methodology

1. Technical and AI crawler access (20): 7 AI search bots (OAI-SearchBot, ChatGPT-User, Claude-SearchBot, Claude-User, PerplexityBot, Perplexity-User, Applebot) must be allowed; 7 training scrapers (GPTBot, ClaudeBot, Google-Extended, CCBot, Meta-ExternalAgent, Bytespider, Amazonbot) are the owner's choice. Also sitemap, response time under 2 s, HTTPS, mixed content, viewport, X-Robots-Tag.

2. LLMs.txt and token hygiene (20): llms.txt presence, duplicate titles and descriptions, signal-to-noise ratio, tokens per page.

3. AEO content structure (20): question headings, BLUF answers (30-75 words directly under a heading), tables, lists, FAQ, title and description length.

4. E-commerce and Schema.org depth (20): Organization, WebSite, Product/Offer, BuyAction/OrderAction, FAQPage, HowTo/Article/VideoObject, image alt coverage.

5. Brand authority and entity graph (20): sameAs completeness, social profiles found on the site but missing from sameAs, NAP consistency, AggregateRating.

Token counts are offline estimates (about 4 chars per token for Latin text, 2 for Cyrillic), good enough for budgeting, not exact tokenizer output.

Development

npm run typecheck && npm run build && npm test

See CONTRIBUTING.md and CLAUDE.md. MIT licensed.