Cloudflare GraphQL Analytics: A Field-Discovery Cookbook When Introspection Is Locked

Cloudflare GraphQL Analytics: A Field-Discovery Cookbook When Introspection Is Locked#

Cloudflare’s GraphQL Analytics API at https://api.cloudflare.com/client/v4/graphql is the richest source of metrics about your CF account — Workers invocations, D1 reads/writes, KV ops, Workers AI neurons, Vectorize queries. The dashboard’s charts are powered by it. The CLI is not: wrangler exposes a fraction of what GraphQL does.

But the schema is hostile to discovery:

  • __type(name: "WorkersInvocationsAdaptive") returns null for almost every node.
  • The official schema docs at developers.cloudflare.com/analytics/graphql-api are partial and stale by months.
  • Nodes like vectorizeQueriesAdaptiveGroups exist, but their sum/dimensions field names are nowhere on the public internet.

You can still derive the schema. The trick is deliberate-error probing: send a query with a guessed field name; the error message tells you whether the parent node exists. This page is the recipe.

The Self-Ask Trap: Why LLMs Are Unreliable Sources About Their Own Quirks

The Self-Ask Trap#

Practitioners ask the LLM about itself as a research shortcut: “What are your common quirks? What temperature should I use? Do you need reasoning_content echoed in multi-turn?” The output looks plausible, often cites specific behaviors, sometimes includes API parameter names. It is often wrong.

The 2026-05-20 kimi-k2.6 tuning research surfaced a clean example. Self-ask said one thing. Documentation, partner adapter source, GitHub issues, and direct API probes said the opposite. The model is provably wrong about itself, and the failure mode is structural — not specific to kimi.