proximens-geo-engine

endpoint

https://www.proximens.nl/mcp

protocol

streamable-http ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is proximens-geo-engine live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is proximens-geo-engine free to use?

Yes — the hub reached it with no key and no payment.

What tools does proximens-geo-engine have?

8 tools: proximens_geo_get_principle, proximens_geo_search_principles, proximens_geo_synthesize_brief, proximens_geo_compare_urls, proximens_geo_bulk_search, proximens_geo_audit_url, proximens_geo_list_categories, proximens_geo_get_stats.

Is proximens-geo-engine safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• proximens_geo_list_categories reads open 15h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "properties": {},
    "description": "No input parameters",
    "additionalProperties": false
  }

  arguments 6 lines copy
• proximens_geo_get_stats reads open 15h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "properties": {},
    "description": "No input parameters",
    "additionalProperties": false
  }

  arguments 6 lines copy
• proximens_geo_get_principle reads unknown 11h ago

  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, 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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "id"
    ],
    "properties": {
      "id": {
        "type": "string",
        "format": "uuid",
        "description": "Principle UUID (from search_principles results)"
      }
    },
    "additionalProperties": false
  }

  arguments 14 lines copy
• proximens_geo_search_principles reads unknown 4h ago

  Semantic search over the Proximens GEO Engine: a curated, continuously-updated knowledge base of 8.000+ Generative Engine Optimization (GEO/AEO) principles, each graded by a 0-1 confidence score and traceable to a source. 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 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.

  mcp-tool

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  {
    "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
  }

  arguments 48 lines copy
• proximens_geo_synthesize_brief reads unknown 4h ago

  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.

  mcp-tool

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  {
    "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
  }

  arguments 40 lines copy
• proximens_geo_compare_urls reads unknown 11h ago

  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.

  mcp-tool

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  {
    "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
  }

  arguments 20 lines copy
• proximens_geo_bulk_search reads unknown 11h ago

  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.

  mcp-tool

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  {
    "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
  }

  arguments 43 lines copy
• proximens_geo_audit_url reads unknown 15h ago

  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.

  mcp-tool

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  {
    "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
  }

  arguments 38 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.