Kaltura MCP Server

Instructions

Implementation Reference

• src/kaltura_mcp/tools/analytics.py:804-902 (handler)

  Main handler function that returns a static JSON document listing all available analytics functions, report types, dimensions, intervals, and other capabilities. No actual API call; purely informational for tool discovery.

  async def list_analytics_capabilities(manager: KalturaClientManager) -> str:
      """
      List all available analytics capabilities and their use cases.
  
      This helper function provides a comprehensive overview of all analytics
      functions, making it easy for LLMs and developers to discover capabilities.
  
      Returns:
          JSON with detailed capability descriptions and examples
      """
      capabilities = {
          "analytics_functions": [
              {
                  "function": "get_analytics",
                  "purpose": "Comprehensive reporting and analysis",
                  "use_cases": [
                      "Performance metrics and rankings",
                      "Detailed breakdowns by category/user/time",
                      "Comparative analysis across content",
                      "Export-ready tabular data",
                  ],
                  "example": "get_analytics(manager, from_date, to_date, report_type='content')",
              },
              {
                  "function": "get_analytics_timeseries",
                  "purpose": "Time-series data for visualization",
                  "use_cases": [
                      "Creating charts and graphs",
                      "Trend analysis over time",
                      "Dashboard visualizations",
                      "Growth tracking",
                  ],
                  "example": "get_analytics_timeseries(manager, from_date, to_date, interval='days')",
              },
              {
                  "function": "get_video_retention",
                  "purpose": "Detailed viewer retention analysis",
                  "use_cases": [
                      "Finding where viewers drop off",
                      "Identifying replay segments",
                      "Optimizing content structure",
                      "Comparing audience segments",
                  ],
                  "example": "get_video_retention(manager, entry_id='1_abc')",
              },
              {
                  "function": "get_realtime_metrics",
                  "purpose": "Live analytics data",
                  "use_cases": [
                      "Monitoring live events",
                      "Real-time dashboards",
                      "Immediate campaign feedback",
                      "Issue detection",
                  ],
                  "example": "get_realtime_metrics(manager, report_type='viewers')",
              },
              {
                  "function": "get_quality_metrics",
                  "purpose": "Streaming quality analysis",
                  "use_cases": [
                      "QoE monitoring",
                      "Playback issue detection",
                      "Infrastructure optimization",
                      "User experience tracking",
                  ],
                  "example": "get_quality_metrics(manager, from_date, to_date)",
              },
              {
                  "function": "get_geographic_breakdown",
                  "purpose": "Location-based analytics",
                  "use_cases": [
                      "Global reach analysis",
                      "Regional content strategy",
                      "Market penetration",
                      "CDN optimization",
                  ],
                  "example": "get_geographic_breakdown(manager, from_date, to_date, granularity='country')",
              },
          ],
          "report_types": list(REPORT_TYPE_MAP.keys()),
          "available_dimensions": [
              "device",
              "country",
              "region",
              "city",
              "domain",
              "entry_id",
              "user_id",
              "application",
              "category",
          ],
          "time_intervals": ["hours", "days", "weeks", "months", "years"],
          "user_filters": ["all", "anonymous", "registered", "specific_user", "cohort"],
          "quality_metrics": ["overview", "experience", "engagement", "stream", "errors"],
          "geographic_levels": ["world", "country", "region", "city"],
      }
  
      return json.dumps(capabilities, indent=2)

• src/kaltura_mcp/server.py:293-300 (schema)

  Tool schema definition in list_tools(), specifying name, detailed description, and empty input schema (no parameters required).

  types.Tool(
      name="list_analytics_capabilities",
      description="Discover ALL analytics capabilities of this system. USE WHEN: User asks 'what analytics can you do?', exploring available reports, understanding metrics options, learning about analytics features. RETURNS: Complete list of 7 analytics functions with descriptions, 60+ report types, available dimensions, time intervals. EXAMPLE: Always run this when user first asks about analytics. No parameters needed - just call it!",
      inputSchema={
          "type": "object",
          "properties": {},
      },
  ),

• src/kaltura_mcp/server.py:513-514 (registration)

  Dispatch logic in call_tool() that routes requests for this tool to the handler function.

  elif name == "list_analytics_capabilities":
      result = await list_analytics_capabilities(kaltura_manager, **arguments)

• src/kaltura_mcp/server.py:44-44 (registration)

  Import of the tool handler into server.py.

  list_analytics_capabilities,

• src/kaltura_mcp/tools/__init__.py:11-11 (helper)

  Export of the tool from tools/__init__.py to make it available for import.

  list_analytics_capabilities,