ወደ ዋናው ይዘት ዝለል

AI: አስተባባሪነት

AI አስተባባሪነት ሦስቱን አንድ ላይ ያስራል፦ ሞተሩ በተጠቃሚ ሁኔታ ውስጥ ቅጽበቱን ይለያል፣ ቡድኑን ይመርጣል እና ድርጊቱን ይመርጣል፣ ስለዚህ ዙሩ እያንዳንዱን ደረጃ በእጅ ሳትገናኙ ይሄዳል።

At a glance

GraphQL
ai query: summarise, explain, suggest
MCP
Every prodantix.* tool, callable by an agent
MCP host
https://eu.mcp.prodantix.com
Auth
Access token
  • በቀጥታ ሁኔታ ውስጥ ትርጉም ያለውን ቅጽበት ይለያል
  • ትክክለኛውን ቡድን እና ትክክለኛውን ድርጊት ይመርጣል
  • ዙሩን ይዘጋዋል፦ ድርጊቱ ሞተሩ የሚማርበት ቀጣዩ ክስተት ይሆናል

እንዴት እንደሚሰራ

Every answer is grounded in rows the caller could already read. Citations are resolved server-side from a built candidate list rather than taken from the model’s reply, so an answer can only point at something that exists and the caller is entitled to see.

የት እንደሚደረስበት

SurfaceOperation
GraphQLai query: summarise, explain, suggest
MCPevery prodantix.* tool, callable by an agent

ምሳሌ

GraphQL
query Explain($projectId: String!) {
  ai {
    explainMetric(
      projectId: $projectId
      metric: "order.completed"
      window: "P7D"
    ) {
      summary
      citations { kind id }
    }
  }
}

Pointing an agent at prodantix

The MCP surface exposes the same capabilities to an autonomous agent. See MCP for the tool list and protocol details.

JSON
{
  "mcpServers": {
    "prodantix": {
      "url": "https://eu.mcp.prodantix.com/mcp",
      "headers": { "Authorization": "Bearer <access token>" }
    }
  }
}