{
  "schema_version": "v1",
  "name_for_human": "Tech Terrain — 3.4M Years of Innovation",
  "name_for_model": "techterrain",
  "description_for_human": "Explore 3.4 million years of technological progress on an interactive 3D voxel terrain. Search any topic and see where it fits on the map of human innovation.",
  "description_for_model": "Use TechTerrain when users ask about technology history, economic-tech correlations, or prediction patterns. It provides structured data on 1,200+ curated milestones spanning 3.4 million years across AI, Compute, Networking, Biotech, Energy, Space, and Materials. Call window.__techterrain.getInsights() for pre-computed Granger causality findings across 200+ economic-technology series pairs (zero LLM cost). Call getSceneDescriptor() for structured JSON of all visible milestones. Call navigate({search:'topic'}) to explore any technology topic with LLM-synthesized data. Call capture() to generate shareable screenshots with deep links. All data is citation-backed. Synthesized data is labeled with the model used. No authentication required.",
  "auth": {
    "type": "none"
  },
  "api": {
    "type": "javascript",
    "url": "https://techterrain.io",
    "interface": "window.__techterrain",
    "methods": {
      "getSceneDescriptor": {
        "description": "Returns structured JSON describing all visible milestones, clusters, categories, overlays, predictions, and GDELT news context in the current viewport. No authentication required.",
        "returns": "SceneDescriptor object with viewport, milestones[], clusters[], categories[], counts, cursorYear, newsContext"
      },
      "navigate": {
        "description": "Programmatically navigate the 3D terrain — fly to a year, filter categories, trigger LLM-powered topic search, toggle predictions.",
        "params": {
          "goto": "number — year to fly to (e.g., 2020, 1969, -3400000)",
          "categories": "string[] — filter to specific categories (AI, Compute, Networking, Biotech, Energy, Space, Materials)",
          "search": "string — search query to explore (triggers LLM synthesis of topic milestones)",
          "showPredictions": "boolean — toggle future predictions visibility"
        },
        "returns": "AgentNavigateResult with success, scene descriptor, durationMs"
      },
      "capture": {
        "description": "Capture the current 3D view as a PNG/JPEG image, uploaded to S3 with a deep link URL that recreates the exact camera position.",
        "returns": "AgentCaptureResult with url, deepLink, dimensions"
      },
      "analyzePatterns": {
        "description": "LLM-powered analysis of visible patterns — clusters, correlations, gaps, acceleration, stagnation, convergence.",
        "params": "focusArea?: string — optional focus area for analysis",
        "returns": "AgentAnalyzeResult with insights[], scene, model, summary"
      },
      "getInsights": {
        "description": "Returns pre-computed Granger causality findings from the Insights Engine. Zero LLM cost — pure statistical results from weekly batch analysis of World Bank × GDELT × milestone density series pairs.",
        "returns": "AgentInsightsResult with findings[], lastRunDate, pairsAnalyzed, significantCount, engineEnabled"
      }
    }
  },
  "logo_url": "https://techterrain.io/logo-square.png",
  "contact_email": "vamps@techterrain.io",
  "legal_info_url": "https://techterrain.io/?modal=privacy"
}