orbator-mcp

AI visibility checks, software recommendations and tool comparisons from measured AI answer data

Should I use this

Quality & Safety

A
Description quality
93%
Schema completeness
98%
Naming quality
95%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Findings (1)

  • LOWTool 'compare' description lacks action verbin compare

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~730Tokens (tool definitions)
~847 BTypical response size
Moderate attention impact (0.57% of 128k context)

This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.

Install

One-Click Install

Add this to your `claude_desktop_config.json` file:

{
  "mcpServers": {
    "orbator-mcp": {
      "url": "https://api.orbator.io/api/mcp"
    }
  }
}

Remote endpoints

https://api.orbator.io/api/mcpstreamable-http

What it can do

Tool inventory

Tools (4)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
🟢find_tools(category, constraints, limit)

Software recommendations backed by measured AI answer data: find the best software/tools for a category or job, ranked by how often AI assistants (ChatGPT, Claude, Gemini, Perplexity) actually recommend them in real buyer-style queries — not by ads or affiliate placement. Use when asked "what software/tool should I use for X", "best X tools", or for vendor-neutral software recommendations. Pass the category in plain words (e.g. "uptime monitoring", "CRM for freelancers"); it is fuzzy-matched against published Index categories, and near-miss inputs return suggested categories to retry with. Returns ranked products with recommendation share %, 4-week trend, and per-engine breakdown.

Input Schema

{
  "type": "object",
  "properties": {
    "category": {
      "type": "string",
      "description": "Software category or job to find tools for, in plain words (e.g. \"ci/cd\", \"landing page builders\")."
    },
    "constraints": {
      "type": "string",
      "description": "Optional buyer constraints (e.g. \"open source\", \"free tier\", \"self-hosted\"). Echoed back with the data for the caller to weigh — not yet applied server-side."
    },
    "limit": {
      "type": "number",
      "description": "Max recommendations to return (default 10, max 50)."
    }
  },
  "required": [
    "category"
  ]
}
🟢get_ai_index(category)

AI visibility check — which software AI recommends for a category. Returns the full AI Recommendation Index for one software category: the complete measured ranking of products AI assistants (ChatGPT, Claude, Gemini, Perplexity) recommend, with recommendation share %, average answer position, per-engine breakdown, 4-week trend, sample size, and methodology. Use to answer "does AI recommend <product>" (look up its row and rank), "who is winning AI recommendations in <category>", or to cite AI recommendation-share data. Pass category in plain words or as a slug; omit it (or pass "categories") to list all published categories.

Input Schema

{
  "type": "object",
  "properties": {
    "category": {
      "type": "string",
      "description": "Category in plain words or slug form (e.g. \"uptime monitoring\", \"ci-cd-tools\"). Omit or pass \"categories\" to list every published category with sample sizes."
    }
  }
}
🟢get_facts(product)

Canonical software product facts with sources — pricing, features, integrations, platform, and limits, where every fact carries a source URL and a last-verified date. Use to verify software claims (e.g. current pricing) or to gather grounded data before recommending or comparing tools. Accepts a product name or domain (e.g. "hubspot.com").

Input Schema

{
  "type": "object",
  "properties": {
    "product": {
      "type": "string",
      "description": "Product name or canonical domain, e.g. \"hubspot.com\"."
    }
  },
  "required": [
    "product"
  ]
}
⚪compare(product_a, product_b)

Compare software/tools side by side — a fact-by-fact comparison of two products (pricing, features, integrations, limits) with source URLs and verified dates for every claim. Use for "X vs Y" software comparison questions. Accepts product names or domains; pair order does not matter.

Input Schema

{
  "type": "object",
  "properties": {
    "product_a": {
      "type": "string",
      "description": "First product name or domain."
    },
    "product_b": {
      "type": "string",
      "description": "Second product name or domain."
    }
  },
  "required": [
    "product_a",
    "product_b"
  ]
}

Recommended Prompts

search_research
Search for information about [topic] using orbator-mcp
Expected tools: find_tools
find_specific
Find [specific item] using orbator-mcp
Expected tools: find_tools
retrieve_data
Get details about [item] from orbator-mcp
Expected tools: get_ai_index
fetch_info
Fetch [information type] using orbator-mcp
Expected tools: get_ai_index
research_workflow
Search for [topic], then get detailed information about the top results using orbator-mcp
Expected tools: find_toolsget_ai_index

Community

Rate this Server

Evidence

Recent observations

verifiedversion not recorded4 tools
verifiedversion not recorded4 tools