AI検索での可視性を高めるためのコンテンツ分析ツール。ChatGPT、Claude、Perplexity、Google AI Overviewsで引用されるために本当に重要な要素を測定します。
クイックナビゲーション
GEOアナライザーは、AIシステムがソースを選択する際に使用するシグナルを分析します：
主張の密度 (Claim Density) - 100単語あたりの抽出可能な事実の数
情報の密度 (Information Density) - 単語数と予測されるAIカバレッジの比較
回答のフロントローディング (Answer Frontloading) - 重要な情報がどれだけ早く提示されるか
意味的トリプル (Semantic Triples) - 構造化された（主語、述語、目的語）関係性
エンティティ認識 (Entity Recognition) - AIが参照可能な固有表現
文の構造 (Sentence Structure) - AI解析に最適な長さ
分析はClaude Sonnet 4.5を使用してローカルで実行されます。外部サービスへのデータ送信はなく、データはマシンから離れません。
Related MCP server: agentaeo-mcp-server
claude_desktop_config.json に以下を追加してください：
{
"mcpServers": {
"geo-analyzer": {
"command": "npx",
"args": ["-y", "@houtini/geo-analyzer@latest"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}設定ファイルの場所:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
保存後、Claude Desktopを再起動してください。
Claude Codeは異なる登録メカニズムを使用しており、claude_desktop_config.json を読み込みません。代わりに claude mcp add を使用してください：
claude mcp add -e ANTHROPIC_API_KEY=sk-ant-... -s user geo-analyzer -- npx -y @houtini/geo-analyzer@latest以下で確認してください：
claude mcp get geo-analyzerStatus: Connected と表示されれば成功です。
Node.js 20以上
Anthropic APIキー (console.anthropic.com)
Analyse https://example.com/article for "topic keywords"トピックのコンテキストは関連性のスコアリングに役立ちますが、必須ではありません：
Analyse https://example.com/article分析対象のコンテンツを貼り付けてください（最低500文字）：
Analyse this content for "sim racing wheels":
[Your content here]詳細な推奨事項を除いた簡潔な出力を取得します：
Analyse https://example.com/article with output_format=summaryスコア | 測定項目 |
総合 (Overall) | 全要素の加重平均 |
抽出可能性 (Extractability) | AIが事実をどれだけ容易に抽出できるか |
可読性 (Readability) | AI解析に適した構造の質 |
引用可能性 (Citability) | 引用・帰属のしやすさ |
情報の密度:
カバレッジ予測を伴う単語数
最適範囲: 800〜1,500単語
1,000単語未満のページ: AIカバレッジ約61%
3,000単語を超えるページ: AIカバレッジ約13%
回答のフロントローディング:
最初の100〜300単語内の主張とエンティティ
最初の主張の位置
回答の即時性を示すスコア
主張の密度:
目標: 100単語あたり4つ以上の主張
抽出可能な事実、統計、測定値
文の長さ:
目標: 平均15〜20単語
Googleの約15.5単語のチャンク抽出と一致
以下を含む優先順位付けされた提案：
コンテンツ内の具体的な場所
修正前後の例
研究に基づいた根拠
公開されているWebページを取得して分析します。
パラメータ | 必須 | 説明 |
| はい | 分析するURL |
| いいえ | 関連性スコアリングのためのトピックコンテキスト |
| いいえ |
|
貼り付けられたコンテンツを直接分析します。
パラメータ | 必須 | 説明 |
| はい | 分析するテキスト (最低500文字) |
| いいえ | 関連性スコアリングのためのトピックコンテキスト |
| いいえ |
|
「ANTHROPIC_API_KEY is required」
設定の env セクションにAPIキーを追加してください。
設定変更後に「Cannot find module」と表示される Claude Desktopを完全に再起動してください。
「Content too short」 意味のある分析には最低500文字が必要です。
ペイウォール（有料記事）でエラーが発生する アナライザーは公開されているページのみにアクセス可能です。
URL分析: 約8〜10秒
テキスト分析: 約5〜7秒
コスト: 分析1回あたり約$0.14 (Sonnet 4.5)
v2.0では外部依存関係が削除されました。設定を更新してください：
旧 (v1.x):
{
"env": {
"GEO_WORKER_URL": "https://...",
"JINA_API_KEY": "jina_..."
}
}新 (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分析手法は、査読済みの研究および実証研究に基づいています：
Aggarwal et al., "GEO: Generative Engine Optimization" - ACM SIGKDD
適用された主な知見：
100単語あたり4つ以上の主張という目標
15〜20単語という最適な文の長さ
抽出可能性に焦点を当てることでAI引用率が40%向上
7,060件のクエリと2,275ページの実証分析
適用された主な知見：
クエリあたりの合計グラウンディング予算は約2,000単語
1位のソースは531単語（予算の28%）を獲得
5位のソースは266単語（予算の13%）を獲得
平均的な抽出チャンク: 15.5単語
1,000単語未満のページ: カバレッジ61%
3,000単語以上のページ: カバレッジ13%
dejan.ai/blog/how-big-are-googles-grounding-chunks dejan.ai/blog/googles-ranking-signals
MIT License - 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.
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Track AI visibility (ChatGPT, Gemini, AI Overviews), research SEO keywords, write, publish, prove.
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Measure how AI engines cite your brand. Cross-engine GEO visibility, as agent tools.