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AI search optimization analysis with actionable findings and gap analysis
AI search optimization analysis with actionable findings and gap analysis
Valid MCP server (2 strong, 4 medium validity signals). 5 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry. Trust signals: trusted author (8/8 approved).
7 files analyzed · 6 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: ANTHROPIC_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-houtini-ai-geo-analyzer": {
"env": {
"ANTHROPIC_API_KEY": "your-anthropic-api-key-here"
},
"args": [
"-y",
"@houtini/geo-analyzer"
],
"command": "npx"
}
}
}From the project's GitHub README.
Content analysis for AI search visibility. Measures what actually matters for getting cited by ChatGPT, Claude, Perplexity, and Google AI Overviews.
Quick Navigation
What it does | Installation | Usage examples | Output | Tools | Troubleshooting | Research foundation
GEO Analyzer examines content for the signals AI systems use when selecting sources to cite:
The analysis runs locally using Claude Sonnet 4.5 for semantic extraction. No external services, no data leaving your machine.
Add to your claude_desktop_config.json:
{
"mcpServers": {
"geo-analyzer": {
"command": "npx",
"args": ["-y", "@houtini/geo-analyzer@latest"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}
Config locations:
%APPDATA%\Claude\claude_desktop_config.json~/Library/Application Support/Claude/claude_desktop_config.json~/.config/Claude/claude_desktop_config.jsonRestart Claude Desktop after saving.
Claude Code uses a different registration mechanism -- it doesn't read claude_desktop_config.json. Use claude mcp add instead:
claude mcp add -e ANTHROPIC_API_KEY=sk-ant-... -s user geo-analyzer -- npx -y @houtini/geo-analyzer@latest
Verify with:
claude mcp get geo-analyzer
You should see Status: Connected.
Analyse https://example.com/article for "topic keywords"
The topic context helps score relevance but isn't required:
Analyse https://example.com/article
Paste content for analysis (minimum 500 characters):
Analyse this content for "sim racing wheels":
[Your content here]
Get condensed output without detailed recommendations:
Analyse https://example.com/article with output_format=summary
| Score | Measures |
|---|---|
| Overall | Weighted average of all factors |
| Extractability | How easily AI can extract facts |
| Readability | Structure quality for AI parsing |
| Citability | How quotable and attributable |
Information Density:
Answer Frontloading:
Claim Density:
Sentence Length:
Prioritised suggestions with:
Fetches and analyses published web pages.
| Parameter | Required | Description |
|---|---|---|
url | Yes | URL to analyse |
query | No | Topic context for relevance scoring |
output_format | No | detailed (default) or summary |
Analyses pasted content directly.
| Parameter | Required | Description |
|---|---|---|
content | Yes | Text to analyse (min 500 chars) |
query | No | Topic context for relevance scoring |
output_format | No | detailed (default) or summary |
"ANTHROPIC_API_KEY is required"
Add your API key to the env section in config.
"Cannot find module" after config change Restart Claude Desktop completely.
"Content too short" Minimum 500 characters required for meaningful analysis.
Paywalled content returns errors The analyser can only access publicly available pages.
v2.0 removed external dependencies. Update your config:
Old (v1.x):
{
"env": {
"GEO_WORKER_URL": "https://...",
"JINA_API_KEY": "jina_..."
}
}
New (v2.x):
{
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
git clone https://github.com/houtini-ai/geo-analyzer.git
cd geo-analyzer
npm install
npm run build
The analysis methodology draws from peer-reviewed research and empirical studies:
Aggarwal et al., "GEO: Generative Engine Optimization" - ACM SIGKDD
Key findings applied:
Empirical analysis of 7,060 queries and 2,275 pages
Key findings applied:
dejan.ai/blog/how-big-are-googles-grounding-chunks
dejan.ai/blog/googles-ranking-signals
MIT License - Houtini.ai
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