Proximens Oracle

io.github.cryptosun/proximens-oracleVersion 1.0.0

1000+ Generative Engine Optimization (GEO) principles exposed via MCP for AI agents.

Status

Responds206 msNov 14, 2026, 14:48 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:48 UTC
Latency: 206 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 21:12 UTC

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Connect

Streamable HTTP

https://www.proximens.nl/mcpResponds206 msNov 14, 2026, 14:48 UTC

No sign-in

Claude Code

claude mcp add --transport http proximens-oracle https://www.proximens.nl/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: proximens-oracle
Remote MCP server URL: https://www.proximens.nl/mcp

Cursor

{
  "mcpServers": {
    "proximens-oracle": {
      "url": "https://www.proximens.nl/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"proximens-oracle","type":"http","url":"https://www.proximens.nl/mcp"}'

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Tools

8 tools

Tools of Proximens Oracle
	Description		Read-only	Idempotent	Open world
`proximens_geo_audit_url`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" with gedeeltelijk=true means the synthesis did not fit the ~50 s budget and the fast, deterministic result was returned instead; deep_mode_reden says why). status="blocked" (login/SSO, HTTP 401/403, bot-challenge) or "unreadable" (no readable text) means NO report was generated — the reason is in error and blokkade. USE fast mode for quick checks and bulk triage; USE deep mode when you need a client-ready audit report. Free tier is blocked.	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" with gedeeltelijk=true means the synthesis did not fit the ~50 s budget and the fast, deterministic result was returned instead; deep_mode_reden says why). status="blocked" (login/SSO, HTTP 401/403, bot-challenge) or "unreadable" (no readable text) means NO report was generated — the reason is in error and blokkade. USE fast mode for quick checks and bulk triage; USE deep mode when you need a client-ready audit report. Free tier is blocked.	No	Yes	No	Yes
`proximens_geo_bulk_search`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.	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.	No	Yes	Yes	No
`proximens_geo_compare_urls`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.	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.	No	Yes	No	Yes
`proximens_geo_get_principle`Fetch one GEO principle from the Proximens GEO Engine by its UUID. INPUT: id (REQUIRED, UUID string taken from a prior search_principles result — call search_principles first if you do not have one). RETURNS: a single principle as JSON with id, title, summary, category and confidence; Pro/Enterprise tiers additionally return full_text, 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.	Fetch one GEO principle from the Proximens GEO Engine by its UUID. INPUT: id (REQUIRED, UUID string taken from a prior search_principles result — call search_principles first if you do not have one). RETURNS: a single principle as JSON with id, title, summary, category and confidence; Pro/Enterprise tiers additionally return full_text, 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.	No	Yes	Yes	No
`proximens_geo_get_stats`Return live aggregate statistics for the Proximens GEO Engine knowledge base. INPUT: none. RETURNS: JSON with total_categories and the last-distillation timestamp; Pro/Enterprise tiers additionally get total_principles (high-confidence count), the full corpus size, a confidence_distribution and the last-validated timestamp. USE WHEN you need to gauge the size and quality of the corpus before relying on it.	Return live aggregate statistics for the Proximens GEO Engine knowledge base. INPUT: none. RETURNS: JSON with total_categories and the last-distillation timestamp; Pro/Enterprise tiers additionally get total_principles (high-confidence count), the full corpus size, a confidence_distribution and the last-validated timestamp. USE WHEN you need to gauge the size and quality of the corpus before relying on it.	No	Yes	Yes	No
`proximens_geo_list_categories`List the GEO principle taxonomy of the Proximens GEO Engine. INPUT: none. RETURNS: JSON with a categories array of {category, description}; Pro/Enterprise tiers additionally get a live count of high-confidence principles per category and 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.	List the GEO principle taxonomy of the Proximens GEO Engine. INPUT: none. RETURNS: JSON with a categories array of {category, description}; Pro/Enterprise tiers additionally get a live count of high-confidence principles per category and 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.	No	Yes	Yes	No
`proximens_geo_search_principles`Semantic search over the Proximens GEO Engine: a curated, continuously-updated knowledge base of Generative Engine Optimization (GEO/AEO) principles built from our own measurements and research, each graded by a 0-1 confidence score. INPUT: query (REQUIRED, natural language, 3-500 chars, e.g. "how does ChatGPT choose which FAQ to cite"); 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. 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.	Semantic search over the Proximens GEO Engine: a curated, continuously-updated knowledge base of Generative Engine Optimization (GEO/AEO) principles built from our own measurements and research, each graded by a 0-1 confidence score. INPUT: query (REQUIRED, natural language, 3-500 chars, e.g. "how does ChatGPT choose which FAQ to cite"); 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. 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.	No	Yes	Yes	No
`proximens_geo_synthesize_brief`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.	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.	No	Yes	No	No

Details

Registry status: Active
Published: May 17, 2026
Updated: May 17, 2026
Website: Not given
Repository: https://github.com/cryptosun/Proximens⁠ (opens in a new tab)
Categories (suggested): AI modelsSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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