Анализ контента для видимости в ИИ-поиске. Измеряет то, что действительно важно для получения цитирований в ChatGPT, Claude, Perplexity и Google AI Overviews.
Быстрая навигация
Что это делает | Установка | Примеры использования | Результаты | Инструменты | Устранение неполадок | Научная база
GEO Analyzer проверяет контент на наличие сигналов, которые ИИ-системы используют при выборе источников для цитирования:
Плотность утверждений (Claim Density) — количество извлекаемых фактов на 100 слов
Информационная плотность — соотношение количества слов и прогнозируемого охвата ИИ
Фронтлоадинг ответов (Answer Frontloading) — как быстро появляется ключевая информация
Семантические триплеты — структурированные отношения (субъект, предикат, объект)
Распознавание сущностей — именованные сущности, на которые может ссылаться ИИ
Структура предложений — оптимальная длина для парсинга ИИ
Анализ выполняется локально с использованием Claude Sonnet 4.5 для семантического извлечения. Никаких внешних сервисов, данные не покидают ваш компьютер.
Related MCP server: agentaeo-mcp-server
Добавьте в ваш claude_desktop_config.json:
{
"mcpServers": {
"geo-analyzer": {
"command": "npx",
"args": ["-y", "@houtini/geo-analyzer@latest"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}Расположение конфигурации:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
После сохранения перезапустите Claude Desktop.
Claude Code использует другой механизм регистрации — он не читает claude_desktop_config.json. Используйте вместо этого claude mcp add:
claude mcp add -e ANTHROPIC_API_KEY=sk-ant-... -s user geo-analyzer -- npx -y @houtini/geo-analyzer@latestПроверьте с помощью:
claude mcp get geo-analyzerВы должны увидеть Status: Connected.
Node.js 20+
API-ключ Anthropic (console.anthropic.com)
Analyse https://example.com/article for "topic keywords"Контекст темы помогает оценить релевантность, но не является обязательным:
Analyse https://example.com/articleВставьте контент для анализа (минимум 500 символов):
Analyse this content for "sim racing wheels":
[Your content here]Получите сжатый вывод без подробных рекомендаций:
Analyse https://example.com/article with output_format=summaryОценка | Что измеряет |
Общая | Взвешенное среднее всех факторов |
Извлекаемость | Насколько легко ИИ может извлечь факты |
Читаемость | Качество структуры для парсинга ИИ |
Цитируемость | Насколько контент пригоден для цитирования |
Информационная плотность:
Количество слов с прогнозом охвата
Оптимальный диапазон: 800-1500 слов
Страницы менее 1 тыс. слов: ~61% охвата ИИ
Страницы более 3 тыс. слов: ~13% охвата ИИ
Фронтлоадинг ответов:
Утверждения и сущности в первых 100/300 словах
Позиция первого утверждения
Оценка, указывающая на немедленность ответа
Плотность утверждений:
Цель: 4+ утверждения на 100 слов
Извлекаемые факты, статистика, измерения
Длина предложений:
Цель: в среднем 15-20 слов
Соответствует извлечению фрагментов Google (~15,5 слов)
Приоритетные предложения с:
Конкретными местами в контенте
Примерами «до/после»
Обоснованием, основанным на исследованиях
Загружает и анализирует опубликованные веб-страницы.
Параметр | Обязательный | Описание |
| Да | URL для анализа |
| Нет | Контекст темы для оценки релевантности |
| Нет |
|
Анализирует вставленный контент напрямую.
Параметр | Обязательный | Описание |
| Да | Текст для анализа (мин. 500 символов) |
| Нет | Контекст темы для оценки релевантности |
| Нет |
|
"ANTHROPIC_API_KEY is required"
Добавьте свой API-ключ в раздел env в конфигурации.
"Cannot find module" после изменения конфигурации Полностью перезапустите Claude Desktop.
"Content too short" Для значимого анализа требуется минимум 500 символов.
Платный контент возвращает ошибки Анализатор может получать доступ только к общедоступным страницам.
Анализ URL: ~8-10 секунд
Анализ текста: ~5-7 секунд
Стоимость: ~$0.14 за анализ (Sonnet 4.5)
v2.0 удалила внешние зависимости. Обновите свою конфигурацию:
Старая (v1.x):
{
"env": {
"GEO_WORKER_URL": "https://...",
"JINA_API_KEY": "jina_..."
}
}Новая (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 buildМетодология анализа опирается на рецензируемые исследования и эмпирические данные:
Aggarwal et al., "GEO: Generative Engine Optimization" - ACM SIGKDD
Примененные ключевые выводы:
Целевая плотность утверждений 4+ на 100 слов
Оптимальная длина предложения 15-20 слов
Улучшение частоты цитирования ИИ на 40% при фокусе на извлекаемость
Эмпирический анализ 7060 запросов и 2275 страниц
Примененные ключевые выводы:
~2000 слов — общий бюджет обоснования (grounding) на запрос
Источник №1 получает 531 слово (28% бюджета)
Источник №5 получает 266 слов (13% бюджета)
Средний фрагмент извлечения: 15,5 слов
Страницы <1 тыс. слов: 61% охвата
Страницы 3 тыс.+ слов: 13% охвата
dejan.ai/blog/how-big-are-googles-grounding-chunks dejan.ai/blog/googles-ranking-signals
Лицензия MIT - 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. Dates show when Glama detected each change.
v3.0.3analyze_textanalyze_urlThe 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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SEO, GEO & AI Visibility — research, write, optimize, publish & monitor content. 122 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.
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