Inhaltsanalyse für die Sichtbarkeit in der KI-Suche. Misst, was tatsächlich wichtig ist, um von ChatGPT, Claude, Perplexity und Google AI Overviews zitiert zu werden.
Schnellnavigation
Was es tut | Installation | Anwendungsbeispiele | Ausgabe | Tools | Fehlerbehebung | Forschungsgrundlage
Der GEO-Analysator untersucht Inhalte auf die Signale, die KI-Systeme bei der Auswahl von Quellen zum Zitieren verwenden:
Anspruchsdichte (Claim Density) - Extrahierbare Fakten pro 100 Wörter
Informationsdichte - Wortanzahl im Vergleich zur vorhergesagten KI-Abdeckung
Antwort-Frontloading - Wie schnell wichtige Informationen erscheinen
Semantische Tripel - Strukturierte (Subjekt, Prädikat, Objekt) Beziehungen
Entitätserkennung - Benannte Entitäten, auf die sich die KI beziehen kann
Satzstruktur - Optimale Länge für die KI-Verarbeitung
Die Analyse erfolgt lokal unter Verwendung von Claude Sonnet 4.5 für die semantische Extraktion. Keine externen Dienste, keine Daten verlassen Ihren Rechner.
Related MCP server: agentaeo-mcp-server
Fügen Sie dies zu Ihrer claude_desktop_config.json hinzu:
{
"mcpServers": {
"geo-analyzer": {
"command": "npx",
"args": ["-y", "@houtini/geo-analyzer@latest"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}Konfigurationspfade:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
Starten Sie Claude Desktop nach dem Speichern neu.
Claude Code verwendet einen anderen Registrierungsmechanismus – er liest nicht die claude_desktop_config.json. Verwenden Sie stattdessen claude mcp add:
claude mcp add -e ANTHROPIC_API_KEY=sk-ant-... -s user geo-analyzer -- npx -y @houtini/geo-analyzer@latestÜberprüfen Sie dies mit:
claude mcp get geo-analyzerSie sollten Status: Connected sehen.
Node.js 20+
Anthropic API-Schlüssel (console.anthropic.com)
Analyse https://example.com/article for "topic keywords"Der Themenkontext hilft bei der Bewertung der Relevanz, ist aber nicht erforderlich:
Analyse https://example.com/articleFügen Sie Inhalte zur Analyse ein (mindestens 500 Zeichen):
Analyse this content for "sim racing wheels":
[Your content here]Erhalten Sie eine komprimierte Ausgabe ohne detaillierte Empfehlungen:
Analyse https://example.com/article with output_format=summaryBewertung | Misst |
Gesamt | Gewichteter Durchschnitt aller Faktoren |
Extrahierbarkeit | Wie leicht die KI Fakten extrahieren kann |
Lesbarkeit | Strukturqualität für die KI-Verarbeitung |
Zitierfähigkeit | Wie zitierfähig und zuordenbar |
Informationsdichte:
Wortanzahl mit Abdeckungsvorhersage
Optimaler Bereich: 800-1.500 Wörter
Seiten unter 1.000 Wörtern: ~61 % KI-Abdeckung
Seiten über 3.000 Wörtern: ~13 % KI-Abdeckung
Antwort-Frontloading:
Ansprüche und Entitäten in den ersten 100/300 Wörtern
Position des ersten Anspruchs
Bewertung, die die Unmittelbarkeit der Antwort angibt
Anspruchsdichte:
Ziel: 4+ Ansprüche pro 100 Wörter
Extrahierbare Fakten, Statistiken, Messungen
Satzlänge:
Ziel: 15-20 Wörter im Durchschnitt
Entspricht Googles ~15,5-Wort-Chunk-Extraktion
Priorisierte Vorschläge mit:
Spezifischen Stellen im Inhalt
Vorher/Nachher-Beispielen
Begründung basierend auf Forschung
Ruft veröffentlichte Webseiten ab und analysiert sie.
Parameter | Erforderlich | Beschreibung |
| Ja | Zu analysierende URL |
| Nein | Themenkontext für Relevanzbewertung |
| Nein |
|
Analysiert eingefügten Inhalt direkt.
Parameter | Erforderlich | Beschreibung |
| Ja | Zu analysierender Text (mind. 500 Zeichen) |
| Nein | Themenkontext für Relevanzbewertung |
| Nein |
|
"ANTHROPIC_API_KEY is required"
Fügen Sie Ihren API-Schlüssel zum env-Abschnitt in der Konfiguration hinzu.
"Cannot find module" nach Konfigurationsänderung Starten Sie Claude Desktop vollständig neu.
"Content too short" Mindestens 500 Zeichen für eine aussagekräftige Analyse erforderlich.
Paywalled-Inhalte führen zu Fehlern Der Analysator kann nur auf öffentlich zugängliche Seiten zugreifen.
URL-Analyse: ~8-10 Sekunden
Textanalyse: ~5-7 Sekunden
Kosten: ~$0,14 pro Analyse (Sonnet 4.5)
v2.0 hat externe Abhängigkeiten entfernt. Aktualisieren Sie Ihre Konfiguration:
Alt (v1.x):
{
"env": {
"GEO_WORKER_URL": "https://...",
"JINA_API_KEY": "jina_..."
}
}Neu (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 buildDie Analysemethodik stützt sich auf Peer-Review-Forschung und empirische Studien:
Aggarwal et al., "GEO: Generative Engine Optimization" - ACM SIGKDD
Angewandte Hauptergebnisse:
Ziel der Anspruchsdichte von 4+ pro 100 Wörter
Optimale Satzlänge von 15-20 Wörtern
40 % Verbesserung der KI-Zitationsraten bei Fokus auf Extrahierbarkeit
Empirische Analyse von 7.060 Suchanfragen und 2.275 Seiten
Angewandte Hauptergebnisse:
~2.000 Wörter Gesamt-Grounding-Budget pro Suchanfrage
Rang #1 Quelle erhält 531 Wörter (28 % des Budgets)
Rang #5 Quelle erhält 266 Wörter (13 % des Budgets)
Durchschnittlicher Extraktions-Chunk: 15,5 Wörter
Seiten <1K Wörter: 61 % Abdeckung
Seiten 3K+ Wörter: 13 % Abdeckung
dejan.ai/blog/how-big-are-googles-grounding-chunks dejan.ai/blog/googles-ranking-signals
MIT-Lizenz - Houtini.ai
Analyze pasted text content for AI search optimization. Performs comprehensive content quality analysis including AI slop detection, writing quality, E-E-A-T signals, data points, originality, and actionability.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The text content to analyze (markdown, plain text, or HTML) | |
| query | No | Optional context string describing the content topic (e.g., "sim racing equipment", "SEO guide"). Used for relevance scoring only. Defaults to "general content analysis". | |
| output_format | No | Output verbosity: "detailed" (default) includes all suggestions and recommendations; "summary" provides condensed results | detailed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions the analysis dimensions and output format options, it lacks critical behavioral details: no information about rate limits, authentication requirements, processing time, error conditions, or what constitutes 'comprehensive' analysis. The description doesn't contradict annotations (none exist), but fails to provide sufficient behavioral context for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey the tool's purpose and scope. The first sentence states the core function, and the second elaborates on analysis dimensions. No redundant or unnecessary information is included. However, it could be slightly more front-loaded by mentioning the key parameters or output options earlier.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 100% schema coverage but no annotations and no output schema, the description is moderately complete. It covers the tool's purpose and analysis dimensions adequately but lacks important contextual information about behavioral characteristics (rate limits, auth needs, processing behavior) and doesn't describe the output format or structure. For a text analysis tool with no output schema, more detail about return values would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal parameter semantics beyond the schema - it mentions 'pasted text content' which aligns with the 'content' parameter, and 'AI search optimization' context which relates to the 'query' parameter's purpose. However, it doesn't provide additional meaning or usage examples beyond what's already in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze pasted text content for AI search optimization' with specific analysis dimensions listed (AI slop detection, writing quality, E-E-A-T signals, etc.). It distinguishes from the sibling tool 'analyze_url' by specifying 'pasted text content' rather than URL analysis. However, it doesn't explicitly contrast with the sibling tool's functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'pasted text content' and 'AI search optimization,' suggesting when this tool is appropriate. It mentions the sibling tool 'analyze_url' exists but provides no explicit guidance on when to use this tool versus that alternative. No exclusion criteria or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze a published URL for AI search optimization. Performs comprehensive content quality analysis including AI slop detection, writing quality, E-E-A-T signals, and actionability.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to analyze | |
| query | No | Optional context string describing the content topic (e.g., "sim racing wheels", "content optimization"). Used for relevance scoring only. Defaults to "general content analysis". | |
| output_format | No | Output verbosity: "detailed" (default) includes all suggestions and recommendations; "summary" provides condensed results | detailed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'comprehensive content quality analysis' but doesn't describe what the analysis returns, potential limitations (e.g., rate limits, authentication needs, or what 'AI slop detection' entails), or side effects. For a tool with no annotations and no output schema, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded: a single sentence that efficiently states the tool's purpose and key analysis components without unnecessary words. Every phrase ('AI search optimization', 'comprehensive content quality analysis', specific detection types) adds value, making it zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analyzing URLs for multiple quality signals) and lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis returns, potential errors, or behavioral traits like rate limits or permissions. The agent is left guessing about the output format and operational constraints, which is inadequate for a tool with no structured output information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'query' affects 'relevance scoring' in more detail or what 'output_format' choices imply beyond the schema's enum). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze a published URL for AI search optimization' with specific components like 'content quality analysis', 'AI slop detection', 'writing quality', 'E-E-A-T signals', and 'actionability'. It distinguishes from the sibling 'analyze_text' by specifying URL analysis rather than text analysis. However, it doesn't explicitly contrast with the sibling tool in the description text itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it implies usage for URL analysis (versus text analysis for the sibling), there's no explicit mention of the sibling tool, prerequisites, or scenarios where this tool is preferred over others. The agent must infer usage context from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v3.0.3analyze_textanalyze_urlScored across 2 tools
The two tools have clearly distinct purposes: analyze_text for pasted text content and analyze_url for published URLs. While their analysis components overlap significantly (both include AI slop detection, writing quality, E-E-A-T signals, and actionability), the input type distinction prevents confusion. The only minor ambiguity is that analyze_text mentions additional features like data points and originality not listed for analyze_url.
Both tools follow a perfect verb_noun pattern with consistent snake_case naming: analyze_text and analyze_url. The naming is completely predictable and readable, with no deviations in style or convention across the tool set.
With only 2 tools for a server named 'GEO Analysis for AI SEO' that suggests geographical and SEO analysis capabilities, the tool count feels too thin. The server's name implies broader functionality (potentially geographical data analysis, keyword research, competitor analysis, etc.), but the tools only cover content analysis of text and URLs, leaving significant gaps in the apparent domain scope.
The tool set is severely incomplete for the server's stated purpose of 'GEO Analysis for AI SEO'. While the two tools provide content quality analysis, there are obvious gaps: no geographical analysis tools (e.g., location-based SEO, regional keyword analysis), no SEO-specific tools (e.g., keyword research, backlink analysis, ranking tracking), and no AI SEO optimization beyond content assessment. This will likely cause agent failures when trying to perform comprehensive GEO or SEO tasks.
SEO, GEO & AI Visibility — research, write, optimize, publish & monitor content. 121 tools.
Track AI visibility (ChatGPT, Gemini, AI Overviews), research SEO keywords, write, publish, prove.
Free AI visibility (GEO/AEO) audit: can ChatGPT, Claude and Perplexity find and cite your website?
Measure how AI engines cite your brand. Cross-engine GEO visibility, as agent tools.