Proximens Oracle
1000+ Generative Engine Optimization (GEO) principles exposed via MCP for AI agents.
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Calidad y seguridad
Hallazgos (1)
- LOWTool 'proximens_geo_search_principles' name length outside 3-30 rangeen proximens_geo_search_principles
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"proximens-oracle": {
"url": "https://www.proximens.nl/mcp"
}
}
}Puntos de conexión remotos
https://www.proximens.nl/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (8)
🟢proximens_geo_search_principles(query, top_k, category, min_confidence)
Semantic search over the Proximens GEO Engine: a curated, continuously-updated knowledge base of 4.000+ verified Generative Engine Optimization (GEO/AEO) principles, each graded by a 0-1 confidence score and traceable to a verified source. INPUT: query (natural language, 3-500 chars); optional category (one of 13 GEO categories), top_k (1-25, default 10), min_confidence (0-1, default 0.5). RETURNS: ranked principles as JSON, each with id, title, summary, category, confidence and a relevance score; Pro/Enterprise tiers additionally return full_text and source. USE WHEN you need evidence-backed answers about how AI search engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot) select, rank and cite web content.
Esquema de entrada
{
"type": "object",
"properties": {
"query": {
"type": "string",
"minLength": 3,
"maxLength": 500,
"description": "Natural-language search query (e.g. \"schema markup for local businesses\" or \"how to optimize for ChatGPT citations\")"
},
"top_k": {
"type": "integer",
"minimum": 1,
"maximum": 25,
"default": 10,
"description": "Number of principles to return (max 25)"
},
"category": {
"type": "string",
"enum": [
"technical",
"structured-data",
"content",
"ai-search",
"freshness",
"multimodal",
"user-signals",
"e-e-a-t",
"mobile",
"performance",
"query-intent",
"internal-linking",
"other"
],
"description": "Filter by category (one of 13 GEO categories)"
},
"min_confidence": {
"type": "number",
"minimum": 0,
"maximum": 1,
"default": 0.5,
"description": "Minimum confidence score (0-1). Default 0.5 filters noise; raise to 0.8+ for high-confidence claims only"
}
},
"required": [
"query"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"properties": {
"results": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"format": "uuid"
},
"title": {
"type": "string"
},
"summary": {
"type": "string"
},
"full_text": {
"type": "string"
},
"category": {
"type": "string"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"source_url": {
"anyOf": [
{
"type": "string",
"format": "uri"
},
{
"type": "null"
}
]
},
"source_type": {
"type": [
"string",
"null"
]
},
"evidence_count": {
"type": "integer",
"minimum": 0
},
"similarity": {
"type": "number",
"minimum": 0,
"maximum": 1,
"description": "Relevance score for the query (0-1)"
},
"upgrade_hint": {
"type": "string"
},
"_wm": {
"type": "string"
}
},
"required": [
"id",
"title",
"summary",
"category",
"confidence"
],
"additionalProperties": false
}
},
"query_used": {
"type": "string"
},
"total_in_database": {
"type": "integer",
"minimum": 0
},
"tier_note": {
"type": "string",
"description": "Free-tier hint when top_k was capped"
}
},
"required": [
"results",
"query_used",
"total_in_database"
],
"additionalProperties": false
}🟢proximens_geo_get_principle(id)
Fetch one GEO principle from the Proximens GEO Engine by its UUID. INPUT: id (UUID, normally taken from a prior search_principles result). RETURNS: a single principle as JSON with id, title, summary, category and confidence; Pro/Enterprise tiers additionally return full_text, source_url, source_type, evidence_count and the last-validated timestamp. USE WHEN you already have a principle id and need its full detail — typically to drill down after search_principles.
Esquema de entrada
{
"type": "object",
"properties": {
"id": {
"type": "string",
"format": "uuid",
"description": "Principle UUID (from search_principles results)"
}
},
"required": [
"id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"properties": {
"id": {
"type": "string",
"format": "uuid"
},
"title": {
"type": "string"
},
"summary": {
"type": "string"
},
"full_text": {
"type": "string"
},
"category": {
"type": "string"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"source_url": {
"anyOf": [
{
"type": "string",
"format": "uri"
},
{
"type": "null"
}
]
},
"source_type": {
"type": [
"string",
"null"
]
},
"evidence_count": {
"type": "integer",
"minimum": 0
},
"similarity": {
"type": [
"number",
"null"
]
},
"upgrade_hint": {
"type": "string"
},
"_wm": {
"type": "string"
},
"source_diversity": {
"type": "integer",
"minimum": 0
},
"last_validated_at": {
"type": [
"string",
"null"
]
},
"branches": {
"type": "array",
"items": {
"anyOf": [
{
"type": "string"
},
{
"type": "object",
"properties": {
"main": {
"type": "string",
"enum": [
"local_services",
"digital_services",
"product_commerce",
"creative_professional",
"health_wellness",
"b2b_saas",
"universal"
]
},
"subs": {
"type": "array",
"items": {
"type": "string"
}
},
"relevance": {
"type": "integer",
"minimum": 0,
"maximum": 3
}
},
"required": [
"main",
"subs",
"relevance"
],
"additionalProperties": false
}
]
}
}
},
"required": [
"id",
"title",
"summary",
"category",
"confidence"
],
"additionalProperties": false
}🟢proximens_geo_list_categories
List the GEO principle taxonomy of the Proximens GEO Engine with a live count of high-confidence principles per category. INPUT: none. RETURNS: JSON with a categories array of {category, count, description} sorted by count, plus a reconciled total that matches get_stats.total_principles. Categories: technical, structured-data, ai-search, content, e-e-a-t, freshness, multimodal, user-signals, performance, query-intent, internal-linking, mobile, other. USE WHEN you want to discover which categories exist before narrowing a search_principles call with the category filter.
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false,
"description": "No input parameters"
}Esquema de salida
{
"type": "object",
"properties": {
"categories": {
"type": "array",
"items": {
"type": "object",
"properties": {
"category": {
"type": "string"
},
"count": {
"type": "integer",
"minimum": 0
},
"description": {
"type": "string"
}
},
"required": [
"category",
"count"
],
"additionalProperties": false
}
},
"total": {
"type": "integer",
"minimum": 0
},
"cached": {
"type": "boolean"
}
},
"required": [
"categories",
"total",
"cached"
],
"additionalProperties": false
}🟢proximens_geo_get_stats
Return live aggregate statistics for the Proximens GEO Engine knowledge base. INPUT: none. RETURNS: JSON with total_principles (high-confidence count), total_categories, and on Pro/Enterprise also extended quality metrics (full corpus size and a confidence_distribution) plus the last-validated timestamp. USE WHEN you need to gauge the size and quality of the corpus before relying on it.
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false,
"description": "No input parameters"
}Esquema de salida
{
"type": "object",
"properties": {
"total_principles": {
"type": "integer",
"minimum": 0
},
"total_evaluated": {
"type": "integer",
"minimum": 0
},
"total_categories": {
"type": "integer",
"minimum": 0
},
"confidence_distribution": {
"type": "object",
"properties": {
">=0.9": {
"type": "integer",
"minimum": 0
},
"0.8-0.9": {
"type": "integer",
"minimum": 0
},
"0.7-0.8": {
"type": "integer",
"minimum": 0
},
"<0.7": {
"type": "integer",
"minimum": 0
}
},
"required": [
">=0.9",
"0.8-0.9",
"0.7-0.8",
"<0.7"
],
"additionalProperties": false
},
"last_validated_at": {
"type": [
"string",
"null"
]
},
"last_distillation_at": {
"type": [
"string",
"null"
]
},
"fetched_at": {
"type": "string"
},
"tier_hint": {
"type": "string"
}
},
"required": [
"total_principles",
"total_categories"
],
"additionalProperties": false
}🟢proximens_geo_audit_url(url, client_name, branche_hint, max_issues, mode)
Pro-tier. Fetch and analyze a web page, then audit it against the Proximens GEO Engine principles across all major GEO dimensions (structured data, crawler access, content depth, freshness, E-E-A-T, multimodal). INPUT: url (required, http/https); optional mode ("fast" = quick signal checks, returns in seconds — the default; "deep" = a full AI-synthesized consultancy report in Dutch with a 7-dimension scorecard and sector benchmark, takes ~30-50s), client_name (report header), branche_hint ("main:sub", e.g. "health_wellness:yoga_studio"), max_issues (1-25, default 10). RETURNS: JSON with a 0-100 score, severity-ranked issues (critical/major/minor) each with a finding and an actionable suggestion, top recommendations, and a markdown report; deep mode additionally returns score_set (7 GEO dimensions), sector (benchmark cohort), and a full consultancy-grade report_markdown (deep_mode="timeout_fallback" means the synthesis exceeded its budget and the fast result was returned instead). USE fast mode for quick checks and bulk triage; USE deep mode when you need a client-ready audit report. Free tier is blocked.
Esquema de entrada
{
"type": "object",
"properties": {
"url": {
"type": "string",
"format": "uri",
"description": "Target URL to audit"
},
"client_name": {
"type": "string",
"description": "Optional client identifier for the audit report header"
},
"branche_hint": {
"type": "string",
"description": "Branche hint in \"main:sub\" format, e.g. \"health_wellness:yoga_studio\". If omitted, principles are matched without branche filter."
},
"max_issues": {
"type": "integer",
"minimum": 1,
"maximum": 25,
"default": 10,
"description": "Maximum issues to return (default 10)"
},
"mode": {
"type": "string",
"enum": [
"fast",
"deep"
],
"default": "fast",
"description": "fast = quick signal checks (seconds); deep = full AI-synthesized consultancy report with sector benchmark (~30-50s)"
}
},
"required": [
"url"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"properties": {
"audit_id": {
"type": "string",
"format": "uuid"
},
"url": {
"type": "string"
},
"status": {
"type": "string",
"enum": [
"complete",
"failed"
]
},
"matched_principles": {
"type": "array",
"items": {
"type": "object",
"properties": {
"principle_id": {
"type": "string",
"format": "uuid"
},
"principle_title": {
"type": "string"
},
"category": {
"type": "string"
},
"severity": {
"type": "string",
"enum": [
"critical",
"major",
"minor"
]
},
"finding": {
"type": "string"
},
"suggestion": {
"type": "string"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"excerpt": {
"type": "string"
}
},
"required": [
"principle_id",
"principle_title",
"category",
"severity",
"finding",
"suggestion",
"confidence"
],
"additionalProperties": false
}
},
"score": {
"type": "number",
"minimum": 0,
"maximum": 100
},
"recommendations": {
"type": "array",
"items": {
"type": "string"
}
},
"report_markdown": {
"type": "string"
},
"signals": {
"type": "object",
"properties": {
"structured_data_valid": {
"type": "integer",
"minimum": 0
},
"structured_data_invalid": {
"type": "integer",
"minimum": 0
},
"schema_types": {
"type": "array",
"items": {
"type": "string"
}
},
"schema_flags": {
"type": "object",
"properties": {
"organization": {
"type": "boolean"
},
"website": {
"type": "boolean"
},
"faqPage": {
"type": "boolean"
},
"product": {
"type": "boolean"
},
"breadcrumbList": {
"type": "boolean"
},
"localBusiness": {
"type": "boolean"
},
"article": {
"type": "boolean"
}
},
"required": [
"organization",
"website",
"faqPage",
"product",
"breadcrumbList",
"localBusiness",
"article"
],
"additionalProperties": false
},
"h1_count": {
"type": "integer",
"minimum": 0
},
"images_total": {
"type": "integer",
"minimum": 0
},
"images_without_alt": {
"type": "integer",
"minimum": 0
},
"has_canonical": {
"type": "boolean"
},
"hreflang_count": {
"type": "integer",
"minimum": 0
},
"has_viewport": {
"type": "boolean"
},
"word_count": {
"type": "integer",
"minimum": 0
},
"render_source": {
"type": "string",
"enum": [
"firecrawl",
"fetch-fallback"
]
}
},
"required": [
"structured_data_valid",
"structured_data_invalid",
"schema_types",
"schema_flags",
"h1_count",
"images_total",
"images_without_alt",
"has_canonical",
"hreflang_count",
"has_viewport",
"word_count",
"render_source"
],
"additionalProperties": false
},
"score_set": {
"type": "object",
"properties": {
"overallGEO": {
"type": [
"number",
"null"
]
},
"aiCitability": {
"type": [
"number",
"null"
]
},
"brandAuthority": {
"type": [
"number",
"null"
]
},
"contentEEAT": {
"type": [
"number",
"null"
]
},
"technicalGEO": {
"type": [
"number",
"null"
]
},
"structuredData": {
"type": [
"number",
"null"
]
},
"platformOptimization": {
"type": [
"number",
"null"
]
}
},
"required": [
"overallGEO",
"aiCitability",
"brandAuthority",
"contentEEAT",
"technicalGEO",
"structuredData",
"platformOptimization"
],
"additionalProperties": false,
"description": "7-dimension GEO scorecard (deep mode only)"
},
"sector": {
"type": "object",
"properties": {
"slug": {
"type": "string"
},
"display_name_nl": {
"type": "string"
}
},
"required": [
"slug",
"display_name_nl"
],
"additionalProperties": false,
"description": "Detected sector benchmark cohort (deep mode only)"
},
"deep_mode": {
"type": "string",
"enum": [
"ok",
"timeout_fallback"
],
"description": "Deep-mode outcome: ok = full synthesized report; timeout_fallback = synthesis exceeded budget, fast result returned"
},
"error": {
"type": "string"
},
"_meta": {
"type": "object",
"properties": {
"tier": {
"type": "string",
"enum": [
"free",
"pro",
"enterprise"
]
},
"rate_limit_remaining": {
"anyOf": [
{
"type": "number"
},
{
"type": "string",
"const": "unlimited"
}
]
},
"processed_at": {
"type": "string"
}
},
"required": [
"tier",
"rate_limit_remaining",
"processed_at"
],
"additionalProperties": false
},
"_wm": {
"type": "string"
}
},
"required": [
"audit_id",
"url",
"status"
],
"additionalProperties": false
}🟢proximens_geo_compare_urls(self_url, competitor_url)
Pro-tier. Fetch two web pages (your URL and a competitor's) and audit both against the Proximens GEO Engine principles using the same audit engine as audit_url, then compute the delta. INPUT: self_url and competitor_url (both required, http/https). RETURNS: JSON with a 0-100 score per URL (same scoring as audit_url), the principles each page satisfies, the principles each page VIOLATES that the other satisfies (delta_principles), and strategic insights on where to close the gap. USE WHEN you want a competitive GEO gap analysis between your page and a rival's.
Esquema de entrada
{
"type": "object",
"properties": {
"self_url": {
"type": "string",
"format": "uri",
"description": "Your URL to audit"
},
"competitor_url": {
"type": "string",
"format": "uri",
"description": "Competitor URL to compare against"
}
},
"required": [
"self_url",
"competitor_url"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"properties": {
"self_url": {
"type": "string"
},
"competitor_url": {
"type": "string"
},
"self_score": {
"type": "number",
"minimum": 0,
"maximum": 100
},
"competitor_score": {
"type": "number",
"minimum": 0,
"maximum": 100
},
"self_matched": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"format": "uuid"
},
"title": {
"type": "string"
},
"summary": {
"type": "string"
},
"full_text": {
"type": "string"
},
"category": {
"type": "string"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"source_url": {
"anyOf": [
{
"type": "string",
"format": "uri"
},
{
"type": "null"
}
]
},
"source_type": {
"type": [
"string",
"null"
]
},
"evidence_count": {
"type": "integer",
"minimum": 0
},
"similarity": {
"type": [
"number",
"null"
]
},
"upgrade_hint": {
"type": "string"
},
"_wm": {
"type": "string"
}
},
"required": [
"id",
"title",
"summary",
"category",
"confidence"
],
"additionalProperties": false
}
},
"competitor_matched": {
"type": "array",
"items": {
"$ref": "#/properties/self_matched/items"
}
},
"delta_principles": {
"type": "object",
"properties": {
"missing_on_self": {
"type": "array",
"items": {
"$ref": "#/properties/self_matched/items"
}
},
"missing_on_competitor": {
"type": "array",
"items": {
"$ref": "#/properties/self_matched/items"
}
}
},
"required": [
"missing_on_self",
"missing_on_competitor"
],
"additionalProperties": false
},
"insights": {
"type": "array",
"items": {
"type": "string"
}
},
"error": {
"type": "string"
},
"_meta": {
"type": "object",
"properties": {
"tier": {
"type": "string",
"enum": [
"free",
"pro",
"enterprise"
]
},
"rate_limit_remaining": {
"anyOf": [
{
"type": "number"
},
{
"type": "string",
"const": "unlimited"
}
]
},
"processed_at": {
"type": "string"
}
},
"required": [
"tier",
"rate_limit_remaining",
"processed_at"
],
"additionalProperties": false
}
},
"required": [
"self_url",
"competitor_url",
"self_score",
"competitor_score",
"self_matched",
"competitor_matched",
"delta_principles",
"insights"
],
"additionalProperties": false
}🟢proximens_geo_synthesize_brief(topic, target_branche, word_count_target, competitor_urls)
Generate a structured, GEO-optimized content brief for a topic using the Proximens GEO Engine. INPUT: topic (3-200 chars); optional target_branche (one of 7 verticals), word_count_target (300-5000, default 1500) and up to 3 competitor_urls. RETURNS: JSON with a suggested H1 and H2 section structure with key points, the principles the content should address, and (Pro/Enterprise) FAQ suggestions and recommended schema.org markup. USE WHEN you need to brief a writer so a page is built to be cited by AI search engines.
Esquema de entrada
{
"type": "object",
"properties": {
"topic": {
"type": "string",
"minLength": 3,
"maxLength": 200
},
"target_branche": {
"type": "string",
"enum": [
"local_services",
"digital_services",
"product_commerce",
"creative_professional",
"health_wellness",
"b2b_saas",
"universal"
]
},
"word_count_target": {
"type": "number",
"minimum": 300,
"maximum": 5000,
"default": 1500
},
"competitor_urls": {
"type": "array",
"items": {
"type": "string",
"format": "uri"
},
"maxItems": 3
}
},
"required": [
"topic"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"properties": {
"brief_id": {
"type": "string",
"format": "uuid"
},
"topic": {
"type": "string"
},
"suggested_structure": {
"type": "object",
"properties": {
"h1": {
"type": "string"
},
"sections": {
"type": "array",
"items": {
"type": "object",
"properties": {
"h2": {
"type": "string"
},
"key_points": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"h2",
"key_points"
],
"additionalProperties": false
}
}
},
"required": [
"h1",
"sections"
],
"additionalProperties": false
},
"principles_to_address": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"format": "uuid"
},
"title": {
"type": "string"
},
"summary": {
"type": "string"
},
"full_text": {
"type": "string"
},
"category": {
"type": "string"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"source_url": {
"anyOf": [
{
"type": "string",
"format": "uri"
},
{
"type": "null"
}
]
},
"source_type": {
"type": [
"string",
"null"
]
},
"evidence_count": {
"type": "integer",
"minimum": 0
},
"similarity": {
"type": [
"number",
"null"
]
},
"upgrade_hint": {
"type": "string"
},
"_wm": {
"type": "string"
}
},
"required": [
"id",
"title",
"summary",
"category",
"confidence"
],
"additionalProperties": false
}
},
"faq_suggestions": {
"type": "array",
"items": {
"type": "object",
"properties": {
"q": {
"type": "string"
},
"a_hint": {
"type": "string"
}
},
"required": [
"q",
"a_hint"
],
"additionalProperties": false
}
},
"schema_markup": {
"type": "array",
"items": {
"type": "object",
"properties": {
"type": {
"type": "string"
},
"rationale": {
"type": "string"
}
},
"required": [
"type",
"rationale"
],
"additionalProperties": false
}
},
"estimated_word_count": {
"type": "number"
},
"_meta": {
"type": "object",
"properties": {
"tier": {
"type": "string",
"enum": [
"free",
"pro",
"enterprise"
]
},
"rate_limit_remaining": {
"anyOf": [
{
"type": "number"
},
{
"type": "string",
"const": "unlimited"
}
]
},
"processed_at": {
"type": "string"
}
},
"required": [
"tier",
"rate_limit_remaining",
"processed_at"
],
"additionalProperties": false
}
},
"required": [
"brief_id",
"topic",
"suggested_structure",
"principles_to_address",
"estimated_word_count"
],
"additionalProperties": false
}🟢proximens_geo_bulk_search(queries, top_k_per_query, category)
Pro-tier. Run many GEO-principle searches in a single fast call. INPUT: queries (array of 2-100 natural-language strings, each 3-500 chars); optional top_k_per_query (1-10, default 5) and category filter. RETURNS: JSON with a results array (per query: the query, its matched principles, and a count), plus total_queries, total_matches and processing time. USE WHEN you need many lookups at once, e.g. a full-site audit or a keyword list, instead of repeated search_principles calls.
Esquema de entrada
{
"type": "object",
"properties": {
"queries": {
"type": "array",
"items": {
"type": "string",
"minLength": 3,
"maxLength": 500
},
"minItems": 2,
"maxItems": 100
},
"top_k_per_query": {
"type": "number",
"minimum": 1,
"maximum": 10,
"default": 5
},
"category": {
"type": "string",
"enum": [
"technical",
"structured-data",
"content",
"ai-search",
"freshness",
"multimodal",
"user-signals",
"e-e-a-t",
"mobile",
"performance",
"query-intent",
"internal-linking",
"other"
]
}
},
"required": [
"queries"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"properties": {
"results": {
"type": "array",
"items": {
"type": "object",
"properties": {
"query": {
"type": "string"
},
"matches": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"format": "uuid"
},
"title": {
"type": "string"
},
"summary": {
"type": "string"
},
"full_text": {
"type": "string"
},
"category": {
"type": "string"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"source_url": {
"anyOf": [
{
"type": "string",
"format": "uri"
},
{
"type": "null"
}
]
},
"source_type": {
"type": [
"string",
"null"
]
},
"evidence_count": {
"type": "integer",
"minimum": 0
},
"similarity": {
"type": [
"number",
"null"
]
},
"upgrade_hint": {
"type": "string"
},
"_wm": {
"type": "string"
}
},
"required": [
"id",
"title",
"summary",
"category",
"confidence"
],
"additionalProperties": false
}
},
"count": {
"type": "number"
}
},
"required": [
"query",
"matches",
"count"
],
"additionalProperties": false
}
},
"total_queries": {
"type": "number"
},
"total_matches": {
"type": "number"
},
"_meta": {
"type": "object",
"properties": {
"tier": {
"type": "string",
"enum": [
"free",
"pro",
"enterprise"
]
},
"rate_limit_remaining": {
"anyOf": [
{
"type": "number"
},
{
"type": "string",
"const": "unlimited"
}
]
},
"processing_time_ms": {
"type": "number"
},
"processed_at": {
"type": "string"
}
},
"required": [
"tier",
"rate_limit_remaining",
"processing_time_ms",
"processed_at"
],
"additionalProperties": false
}
},
"required": [
"results",
"total_queries",
"total_matches"
],
"additionalProperties": false
}Comunidad
Evidencia