MCP Radar
MCP Server

Proximens Oracle

io.github.cryptosun/proximens-oracle
Marketing & Advertising Public & reachable MCP 2025-11-25

What this MCP does

Searches, audits, compares, and applies a knowledge base of Generative Engine Optimization principles to web content.

Capabilities

Tools

8 tools
proximens_geo_audit_url
Audit URL against Proximens GEO Engine
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.
Read only Open world
Input schema 
{'type': 'object', 'required': ['url'], 'properties': {'url': {'type': 'string', 'format': 'uri', 'description': 'Target URL to audit'}, 'mode': {'enum': ['fast', 'deep'], 'type': 'string', 'default': 'fast', 'description': 'fast = quick signal checks (seconds); deep = full AI-synthesized consultancy report with sector benchmark (~30-50s)'}, 'max_issues': {'type': 'integer', 'default': 10, 'maximum': 25, 'minimum': 1, 'description': 'Maximum issues to return (default 10)'}, '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.'}}, 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['audit_id', 'url', 'status'], 'properties': {'_wm': {'type': 'string'}, 'url': {'type': 'string'}, '_meta': {'type': 'object', 'required': ['tier', 'rate_limit_remaining', 'processed_at'], 'properties': {'tier': {'enum': ['free', 'pro', 'enterprise'], 'type': 'string'}, 'processed_at': {'type': 'string'}, 'rate_limit_remaining': {'anyOf': [{'type': 'number'}, {'type': 'string', 'const': 'unlimited'}]}}, 'additionalProperties': False}, 'error': {'type': 'string'}, 'score': {'type': 'number', 'maximum': 100, 'minimum': 0}, 'sector': {'type': 'object', 'required': ['slug', 'display_name_nl'], 'properties': {'slug': {'type': 'string'}, 'display_name_nl': {'type': 'string'}}, 'description': 'Detected sector benchmark cohort (deep mode only)', 'additionalProperties': False}, 'status': {'enum': ['complete', 'failed'], 'type': 'string'}, 'signals': {'type': 'object', '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'], 'properties': {'h1_count': {'type': 'integer', 'minimum': 0}, 'word_count': {'type': 'integer', 'minimum': 0}, 'has_viewport': {'type': 'boolean'}, 'images_total': {'type': 'integer', 'minimum': 0}, 'schema_flags': {'type': 'object', 'required': ['organization', 'website', 'faqPage', 'product', 'breadcrumbList', 'localBusiness', 'article'], 'properties': {'article': {'type': 'boolean'}, 'faqPage': {'type': 'boolean'}, 'product': {'type': 'boolean'}, 'website': {'type': 'boolean'}, 'organization': {'type': 'boolean'}, 'localBusiness': {'type': 'boolean'}, 'breadcrumbList': {'type': 'boolean'}}, 'additionalProperties': False}, 'schema_types': {'type': 'array', 'items': {'type': 'string'}}, 'has_canonical': {'type': 'boolean'}, 'render_source': {'enum': ['firecrawl', 'fetch-fallback'], 'type': 'string'}, 'hreflang_count': {'type': 'integer', 'minimum': 0}, 'images_without_alt': {'type': 'integer', 'minimum': 0}, 'structured_data_valid': {'type': 'integer', 'minimum': 0}, 'structured_data_invalid': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}, 'audit_id': {'type': 'string', 'format': 'uuid'}, 'deep_mode': {'enum': ['ok', 'timeout_fallback'], 'type': 'string', 'description': 'Deep-mode outcome: ok = full synthesized report; timeout_fallback = synthesis exceeded budget, fast result returned'}, 'score_set': {'type': 'object', 'required': ['overallGEO', 'aiCitability', 'brandAuthority', 'contentEEAT', 'technicalGEO', 'structuredData', 'platformOptimization'], 'properties': {'overallGEO': {'type': ['number', 'null']}, 'contentEEAT': {'type': ['number', 'null']}, 'aiCitability': {'type': ['number', 'null']}, 'technicalGEO': {'type': ['number', 'null']}, 'brandAuthority': {'type': ['number', 'null']}, 'structuredData': {'type': ['number', 'null']}, 'platformOptimization': {'type': ['number', 'null']}}, 'description': '7-dimension GEO scorecard (deep mode only)', 'additionalProperties': False}, 'recommendations': {'type': 'array', 'items': {'type': 'string'}}, 'report_markdown': {'type': 'string'}, 'matched_principles': {'type': 'array', 'items': {'type': 'object', 'required': ['principle_id', 'principle_title', 'category', 'severity', 'finding', 'suggestion', 'confidence'], 'properties': {'excerpt': {'type': 'string'}, 'finding': {'type': 'string'}, 'category': {'type': 'string'}, 'severity': {'enum': ['critical', 'major', 'minor'], 'type': 'string'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'suggestion': {'type': 'string'}, 'principle_id': {'type': 'string', 'format': 'uuid'}, 'principle_title': {'type': 'string'}}, 'additionalProperties': False}}}, 'additionalProperties': False}
proximens_geo_bulk_search
Bulk Search Proximens GEO Engine
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.
Read only Idempotent
Input schema 
{'type': 'object', 'required': ['queries'], 'properties': {'queries': {'type': 'array', 'items': {'type': 'string', 'maxLength': 500, 'minLength': 3}, 'maxItems': 100, 'minItems': 2}, 'category': {'enum': ['technical', 'structured-data', 'content', 'ai-search', 'freshness', 'multimodal', 'user-signals', 'e-e-a-t', 'mobile', 'performance', 'query-intent', 'internal-linking', 'other'], 'type': 'string'}, 'top_k_per_query': {'type': 'number', 'default': 5, 'maximum': 10, 'minimum': 1}}, 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['results', 'total_queries', 'total_matches'], 'properties': {'_meta': {'type': 'object', 'required': ['tier', 'rate_limit_remaining', 'processing_time_ms', 'processed_at'], 'properties': {'tier': {'enum': ['free', 'pro', 'enterprise'], 'type': 'string'}, 'processed_at': {'type': 'string'}, 'processing_time_ms': {'type': 'number'}, 'rate_limit_remaining': {'anyOf': [{'type': 'number'}, {'type': 'string', 'const': 'unlimited'}]}}, 'additionalProperties': False}, 'results': {'type': 'array', 'items': {'type': 'object', 'required': ['query', 'matches', 'count'], 'properties': {'count': {'type': 'number'}, 'query': {'type': 'string'}, 'matches': {'type': 'array', 'items': {'type': 'object', 'required': ['id', 'title', 'summary', 'category', 'confidence'], 'properties': {'id': {'type': 'string', 'format': 'uuid'}, '_wm': {'type': 'string'}, 'title': {'type': 'string'}, 'summary': {'type': 'string'}, 'category': {'type': 'string'}, 'full_text': {'type': 'string'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'similarity': {'type': ['number', 'null']}, 'source_url': {'anyOf': [{'type': 'string', 'format': 'uri'}, {'type': 'null'}]}, 'source_type': {'type': ['string', 'null']}, 'upgrade_hint': {'type': 'string'}, 'evidence_count': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}}}, 'additionalProperties': False}}, 'total_matches': {'type': 'number'}, 'total_queries': {'type': 'number'}}, 'additionalProperties': False}
proximens_geo_compare_urls
Compare URLs against Proximens GEO Engine
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.
Read only Open world
Input schema 
{'type': 'object', 'required': ['self_url', 'competitor_url'], 'properties': {'self_url': {'type': 'string', 'format': 'uri', 'description': 'Your URL to audit'}, 'competitor_url': {'type': 'string', 'format': 'uri', 'description': 'Competitor URL to compare against'}}, 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['self_url', 'competitor_url', 'self_score', 'competitor_score', 'self_matched', 'competitor_matched', 'delta_principles', 'insights'], 'properties': {'_meta': {'type': 'object', 'required': ['tier', 'rate_limit_remaining', 'processed_at'], 'properties': {'tier': {'enum': ['free', 'pro', 'enterprise'], 'type': 'string'}, 'processed_at': {'type': 'string'}, 'rate_limit_remaining': {'anyOf': [{'type': 'number'}, {'type': 'string', 'const': 'unlimited'}]}}, 'additionalProperties': False}, 'error': {'type': 'string'}, 'insights': {'type': 'array', 'items': {'type': 'string'}}, 'self_url': {'type': 'string'}, 'self_score': {'type': 'number', 'maximum': 100, 'minimum': 0}, 'self_matched': {'type': 'array', 'items': {'type': 'object', 'required': ['id', 'title', 'summary', 'category', 'confidence'], 'properties': {'id': {'type': 'string', 'format': 'uuid'}, '_wm': {'type': 'string'}, 'title': {'type': 'string'}, 'summary': {'type': 'string'}, 'category': {'type': 'string'}, 'full_text': {'type': 'string'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'similarity': {'type': ['number', 'null']}, 'source_url': {'anyOf': [{'type': 'string', 'format': 'uri'}, {'type': 'null'}]}, 'source_type': {'type': ['string', 'null']}, 'upgrade_hint': {'type': 'string'}, 'evidence_count': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}}, 'competitor_url': {'type': 'string'}, 'competitor_score': {'type': 'number', 'maximum': 100, 'minimum': 0}, 'delta_principles': {'type': 'object', 'required': ['missing_on_self', 'missing_on_competitor'], 'properties': {'missing_on_self': {'type': 'array', 'items': {'$ref': '#/properties/self_matched/items'}}, 'missing_on_competitor': {'type': 'array', 'items': {'$ref': '#/properties/self_matched/items'}}}, 'additionalProperties': False}, 'competitor_matched': {'type': 'array', 'items': {'$ref': '#/properties/self_matched/items'}}}, 'additionalProperties': False}
proximens_geo_get_principle
Get GEO principle by 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.
Read only Idempotent
Input schema 
{'type': 'object', 'required': ['id'], 'properties': {'id': {'type': 'string', 'format': 'uuid', 'description': 'Principle UUID (from search_principles results)'}}, 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['id', 'title', 'summary', 'category', 'confidence'], 'properties': {'id': {'type': 'string', 'format': 'uuid'}, '_wm': {'type': 'string'}, 'title': {'type': 'string'}, 'summary': {'type': 'string'}, 'branches': {'type': 'array', 'items': {'anyOf': [{'type': 'string'}, {'type': 'object', 'required': ['main', 'subs', 'relevance'], 'properties': {'main': {'enum': ['local_services', 'digital_services', 'product_commerce', 'creative_professional', 'health_wellness', 'b2b_saas', 'universal'], 'type': 'string'}, 'subs': {'type': 'array', 'items': {'type': 'string'}}, 'relevance': {'type': 'integer', 'maximum': 3, 'minimum': 0}}, 'additionalProperties': False}]}}, 'category': {'type': 'string'}, 'full_text': {'type': 'string'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'similarity': {'type': ['number', 'null']}, 'source_url': {'anyOf': [{'type': 'string', 'format': 'uri'}, {'type': 'null'}]}, 'source_type': {'type': ['string', 'null']}, 'upgrade_hint': {'type': 'string'}, 'evidence_count': {'type': 'integer', 'minimum': 0}, 'source_diversity': {'type': 'integer', 'minimum': 0}, 'last_validated_at': {'type': ['string', 'null']}}, 'additionalProperties': False}
proximens_geo_get_stats
Get GEO Engine statistics
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.
Read only Idempotent
Input schema 
{'type': 'object', 'properties': {}, 'description': 'No input parameters', 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['total_principles', 'total_categories'], 'properties': {'tier_hint': {'type': 'string'}, 'fetched_at': {'type': 'string'}, 'total_evaluated': {'type': 'integer', 'minimum': 0}, 'total_categories': {'type': 'integer', 'minimum': 0}, 'total_principles': {'type': 'integer', 'minimum': 0}, 'last_validated_at': {'type': ['string', 'null']}, 'last_distillation_at': {'type': ['string', 'null']}, 'confidence_distribution': {'type': 'object', 'required': ['>=0.9', '0.8-0.9', '0.7-0.8', '<0.7'], 'properties': {'<0.7': {'type': 'integer', 'minimum': 0}, '>=0.9': {'type': 'integer', 'minimum': 0}, '0.7-0.8': {'type': 'integer', 'minimum': 0}, '0.8-0.9': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}}, 'additionalProperties': False}
proximens_geo_list_categories
List GEO principle 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.
Read only Idempotent
Input schema 
{'type': 'object', 'properties': {}, 'description': 'No input parameters', 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['categories', 'total', 'cached'], 'properties': {'total': {'type': 'integer', 'minimum': 0}, 'cached': {'type': 'boolean'}, 'categories': {'type': 'array', 'items': {'type': 'object', 'required': ['category', 'count'], 'properties': {'count': {'type': 'integer', 'minimum': 0}, 'category': {'type': 'string'}, 'description': {'type': 'string'}}, 'additionalProperties': False}}}, 'additionalProperties': False}
proximens_geo_search_principles
Search GEO principles (Proximens GEO Engine)
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.
Read only Idempotent
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'query': {'type': 'string', 'maxLength': 500, 'minLength': 3, 'description': 'Natural-language search query (e.g. "schema markup for local businesses" or "how to optimize for ChatGPT citations")'}, 'top_k': {'type': 'integer', 'default': 10, 'maximum': 25, 'minimum': 1, 'description': 'Number of principles to return (max 25)'}, 'category': {'enum': ['technical', 'structured-data', 'content', 'ai-search', 'freshness', 'multimodal', 'user-signals', 'e-e-a-t', 'mobile', 'performance', 'query-intent', 'internal-linking', 'other'], 'type': 'string', 'description': 'Filter by category (one of 13 GEO categories)'}, 'min_confidence': {'type': 'number', 'default': 0.5, 'maximum': 1, 'minimum': 0, 'description': 'Minimum confidence score (0-1). Default 0.5 filters noise; raise to 0.8+ for high-confidence claims only'}}, 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['results', 'query_used', 'total_in_database'], 'properties': {'results': {'type': 'array', 'items': {'type': 'object', 'required': ['id', 'title', 'summary', 'category', 'confidence'], 'properties': {'id': {'type': 'string', 'format': 'uuid'}, '_wm': {'type': 'string'}, 'title': {'type': 'string'}, 'summary': {'type': 'string'}, 'category': {'type': 'string'}, 'full_text': {'type': 'string'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'similarity': {'type': 'number', 'maximum': 1, 'minimum': 0, 'description': 'Relevance score for the query (0-1)'}, 'source_url': {'anyOf': [{'type': 'string', 'format': 'uri'}, {'type': 'null'}]}, 'source_type': {'type': ['string', 'null']}, 'upgrade_hint': {'type': 'string'}, 'evidence_count': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}}, 'tier_note': {'type': 'string', 'description': 'Free-tier hint when top_k was capped'}, 'query_used': {'type': 'string'}, 'total_in_database': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}
proximens_geo_synthesize_brief
Synthesize Content Brief from Proximens GEO Engine
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.
Read only
Input schema 
{'type': 'object', 'required': ['topic'], 'properties': {'topic': {'type': 'string', 'maxLength': 200, 'minLength': 3}, 'target_branche': {'enum': ['local_services', 'digital_services', 'product_commerce', 'creative_professional', 'health_wellness', 'b2b_saas', 'universal'], 'type': 'string'}, 'competitor_urls': {'type': 'array', 'items': {'type': 'string', 'format': 'uri'}, 'maxItems': 3}, 'word_count_target': {'type': 'number', 'default': 1500, 'maximum': 5000, 'minimum': 300}}, 'additionalProperties': False}
Output schema 
{'type': 'object', 'required': ['brief_id', 'topic', 'suggested_structure', 'principles_to_address', 'estimated_word_count'], 'properties': {'_meta': {'type': 'object', 'required': ['tier', 'rate_limit_remaining', 'processed_at'], 'properties': {'tier': {'enum': ['free', 'pro', 'enterprise'], 'type': 'string'}, 'processed_at': {'type': 'string'}, 'rate_limit_remaining': {'anyOf': [{'type': 'number'}, {'type': 'string', 'const': 'unlimited'}]}}, 'additionalProperties': False}, 'topic': {'type': 'string'}, 'brief_id': {'type': 'string', 'format': 'uuid'}, 'schema_markup': {'type': 'array', 'items': {'type': 'object', 'required': ['type', 'rationale'], 'properties': {'type': {'type': 'string'}, 'rationale': {'type': 'string'}}, 'additionalProperties': False}}, 'faq_suggestions': {'type': 'array', 'items': {'type': 'object', 'required': ['q', 'a_hint'], 'properties': {'q': {'type': 'string'}, 'a_hint': {'type': 'string'}}, 'additionalProperties': False}}, 'suggested_structure': {'type': 'object', 'required': ['h1', 'sections'], 'properties': {'h1': {'type': 'string'}, 'sections': {'type': 'array', 'items': {'type': 'object', 'required': ['h2', 'key_points'], 'properties': {'h2': {'type': 'string'}, 'key_points': {'type': 'array', 'items': {'type': 'string'}}}, 'additionalProperties': False}}}, 'additionalProperties': False}, 'estimated_word_count': {'type': 'number'}, 'principles_to_address': {'type': 'array', 'items': {'type': 'object', 'required': ['id', 'title', 'summary', 'category', 'confidence'], 'properties': {'id': {'type': 'string', 'format': 'uuid'}, '_wm': {'type': 'string'}, 'title': {'type': 'string'}, 'summary': {'type': 'string'}, 'category': {'type': 'string'}, 'full_text': {'type': 'string'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0}, 'similarity': {'type': ['number', 'null']}, 'source_url': {'anyOf': [{'type': 'string', 'format': 'uri'}, {'type': 'null'}]}, 'source_type': {'type': ['string', 'null']}, 'upgrade_hint': {'type': 'string'}, 'evidence_count': {'type': 'integer', 'minimum': 0}}, 'additionalProperties': False}}}, 'additionalProperties': False}
History

Recent tool changes

Added
proximens_geo_bulk_search
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_synthesize_brief
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_compare_urls
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_audit_url
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_get_stats
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_list_categories
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_get_principle
Sept. 17, 2026, 12:41 p.m.
Added
proximens_geo_search_principles
Sept. 17, 2026, 12:41 p.m.
Discover

Similar MCP servers

AdButler

com.adbutler/mcp-server

Manages AdButler advertisers, campaigns, ad items, audiences, zones, creatives, catalogs, and advertising reports.

neuron

io.github.conquext/neuron

Operates AI chatbots and messaging workflows across WhatsApp and social channels, including contacts, campaigns, conversations, p…

SharksAPI.AI

ai.sharksapi/marketing

Provides marketing analytics and actions across GA4, Search Console, Google Ads, social channels, SEO, WordPress, and marketing p…

Meta Council

com.meta-council/decision-intelligence

Provides governed workflows for consulting, outreach, sales deals, invoices, marketing campaigns, content approvals, accounting a…

synter-ads

io.github.Synter-Media-AI/synter-ads

Manages cross-platform advertising campaigns, audiences, tracking, creative, landing pages, campaign audits, technology intellige…

synter-ads

io.github.jshorwitz/synter-ads

Manages cross-platform advertising campaigns, audiences, tracking, creative, landing pages, campaign audits, technology intellige…

Stratalize Intelligence

com.stratalize/intelligence

Provides vendor, market, SaaS, workforce, government contract, public company, regulatory, competitive, and AI-brand intelligence…

Hermoso

io.github.hermoso-ai/hermoso

Supports ad research, competitor analysis, video and static ad creation, campaign policy checks, content publishing, and performa…