AI Workflows for MCP Server and Platform Telemetry Reporting | Diaphora

Platform Analytics

Know which MCP servers earn their keep. Without the joins.

Every question about fleet health is a manual join across telemetry tables. That's not judgment — it's plumbing. These blueprints make the join, and hand back the same snapshot on a schedule.

Parameters

Tools

Adoption

Typed output

What breaks when you do this by hand

What you get instead

This is the whole thing

The opening of MCP Server Insights — the system prompt, its typed parameters, and the tools it's allowed to reach, all declared up front. No canvas, no hidden nodes. 207 lines of source you can review in a pull request.

system("You are an analytical assistant querying BigQuery for MCP server telemetry.")

parameter("servername", type=string, title="Server Name")

require mcp BigQuery

components {
    schema("TenantStats") {
        tenant_name: string
        tenant_id: string
        status: string
        total_tool_calls: int
        total_errors: int
        error_rate: float
        active_users: int
        distinct_tools_called: int
        active_days: int
        first_call: string
        last_call: string
        days_since_last_call: int
        connected_but_never_called: bool
    }
}

2 blueprints you can run today

Every one is a typed FML plan. Open it, read the source, and run it — nothing here is a mockup.

What every blueprint here guarantees