I Asked My AI "Who Are You" — It Cost 42,000 Tokens to Answer

A developer discovered that asking Claude Code 'who are you' triggered a 118,693-byte request, with the actual 19-byte question comprising just 0.016% of the payload. The bulk consisted of 38 tool JSON schemas (78,935 bytes), a system prompt (22,178 bytes), and deferred tool listings (16,844 bytes), none of which were used to answer the query.

I typed a 19-character question into Claude Code: who are you? Then I opened the network logs of what my machine actually sent over the wire. I recommend you never do this. It ruins something in you. My question was 19 bytes . The request that carried it was 118,693 bytes . That's a 6,200x amplification before the model reads a single word I wrote. My actual message was 0.016% of the payload. If this were a letter, I'd be mailing a phone book with a Post-it note inside. Here's the pie, byte for byte: | What | Bytes | Share | |---|---|---| | 38 tool JSON schemas | 78,935 | 66.8% | | System prompt behavior manual, safety rules, my git status | 22,178 | 18.8% | A list of 60 more tools it could load, plus 25 skill descriptions | 16,844 | 14.3% | | My memory files, my email address, today's date | 1,126 | 1.0% | My question | 19 | 0.016% | And the tool schemas aren't even schemas, mostly. Of those 78,935 bytes, only 29,164 are machine-readable JSON schema. The other 44,392 bytes are English prose — instruction manuals for the model, shipped on every single request. One tool alone, Workflow , carries a 19,012-byte description. That's a small essay. It rides along on every message I send, forever, whether I orchestrate a multi-agent workflow or just say "hi." Sixteen of the 38 tools are for driving a browser preview I never opened. There's an iOS Simulator controller in there. I was asking a chatbot its name. Every tool schema in that request, largest first. "Prose" is the English description; "schema" is the actual machine-readable part. Approximate tokens = bytes ÷ 4. | Tool | Bytes | Prose | Schema | ~Tokens | Did I need it? | |---|---|---|---|---|---| | Workflow multi-agent orchestration | 21,609 | 19,012 | 1,833 | ~5,400 | No | | Artifact publish web pages | 10,679 | 6,358 | 4,002 | ~2,670 | No | | AskUserQuestion | 4,371 | 1,097 | 3,149 | ~1,090 | No | | iOS Simulator control | 3,976 | 1,307 | 2,518 | ~990 | No | | ScheduleWakeup self-pacing loops | 3,942 | 2,685 | 1,068 | ~985 | No | | visualize show widget | 3,038 | 648 | 2,259 | ~760 | No | | Agent spawn subagents | 3,037 | 1,574 | 1,342 | ~760 | No | | Bash | 2,860 | 1,290 | 1,456 | ~715 | No but fair | | ReportFindings code review UI | 2,314 | 574 | 1,646 | ~580 | No | | Browser computer mouse/keyboard | 2,141 | 193 | 1,837 | ~535 | No | | Skill | 1,699 | 1,244 | 345 | ~425 | No | | Read | 1,670 | 790 | 776 | ~420 | No | | Browser preview start | 1,656 | 1,328 | 143 | ~415 | No | | spawn task background task chips | 1,553 | 664 | 765 | ~390 | No | | ToolSearch loads MORE tools | 1,522 | 953 | 427 | ~380 | No | | mark chapter session table of contents | 1,092 | 699 | 256 | ~275 | No | | Edit | 1,037 | 360 | 584 | ~260 | No | | dismiss task | 918 | 560 | 234 | ~230 | No | | + 20 more browser tabs, navigation, console readers, form filler, widget reader, Write… | ~9,800 | — | — | ~2,450 | No | Total | 78,935 | 44,392 | 29,164 | ~19,700 | 0 of 38 used | Read that last column again. The model answered my question using zero of the 38 tools it was handed. Then notice ToolSearch : a tool whose only purpose is to fetch even more tools — there are ~60 additional ones WebSearch, WebFetch, cron schedulers, task managers, notebook editors, a whole second browser that drives my real Chrome, password-manager integration waiting behind it, listed by name in that 16.8KB deferred-tools block. The request contains a tool for requesting tools. We have achieved tool-calling recursion. The prose-to-schema ratio is the tell. Workflow 's description is 19KB of instruction manual — pipeline semantics, barrier vs. no-barrier philosophy, seven design patterns with code examples, a "smell test" for when you're using it wrong. It's genuinely well-written documentation. It is also transmitted to a datacenter every time I type "ok". I audited my own machine after this. Here's the honest control surface, verified against my actual config files — with guesses labeled as guesses. Things I verified you CAN control: ~/.claude/settings.json . Delete the hook block, it's gone. ~/.claude/projects/<project /memory/ . Delete them, they stop being sent. The app injecting my email address alongside them — that part has no knob. anthropic-skills plugin. Uninstalling it removes ~25 skill descriptions from every request. DISABLE TELEMETRY=1 , DISABLE ERROR REPORTING=1 , CLAUDE CODE DISABLE NONESSENTIAL TRAFFIC=1 are documented switches for the ~600KB tengu firehose. Documented — I haven't packet-captured a session with them on yet. Trust, but HAR-verify. claude doesn't load. Using the terminal is the single biggest schema diet available to a normal user. Things you CANNOT control, period: tengu workflows enabled: true , tengu report findings tool: true , tengu chrome auto enable: true . There is a --disallowedTools launch flag on the CLI, but the desktop app exposes no such thing in its UI. .claude.json is a cache that re-syncs on their schedule. I found flags in mine for experiments I've never seen, promo banners, A/B copy tests, per-model "velvet hammer" variants twenty-two of those, whatever they are , and two remote kill switches — tengu-off-switch and tengu-fable-off-switch — that Anthropic can flip and I cannot. metadata of every model call. OS, arch, node version, shell, package managers go in the telemetry. None of it optional.The asymmetry is the story. The user's levers — a hook, some markdown files, one plugin, an env var — govern maybe 3% of the payload. The other 97% is decided by people you've never met, toggled by flags with names like tengu amber moleskin , shipped to you on every keystroke, and cached with a straight face. The response to my question was 127 tokens — 47 of them the model privately thinking "user's being blunt, keep it brief," and 80 tokens of actual answer. Generating those 127 tokens takes well under a second. The other ~8 seconds? The model had to ingest 42,270 tokens of context first — and 13,445 of them weren't in the prompt cache yet, because this was the session's first message. I paid an eight-second cold-start tax so the model could re-read the operating manual for a browser I never launched. The economics in one line: 42,270 tokens in, 127 tokens out. A 333:1 read-to-write ratio. To say hello. While answering me, the app quietly made a second, parallel model call — to a different, smaller model — whose entire job was to invent a title for the sidebar. Input: my rude question. Output: "Clarify Claude agent identity" . Five hundred twenty-one tokens of prompt to produce four polite words I never asked for. After the answer landed, the client fired 40 more API calls in one burst — all to a token-counting endpoint. Why 40? It was measuring itself: one call per slice of its own system prompt, plus one call per tool sending the string "foo" next to a single schema, so it could subtract the baseline and learn each tool's exact token weight. The app literally runs a scientific experiment on its own bloat, every session, to render a little context meter. And the telemetry. Oh, the telemetry. In the same 15-second window: ~600KB across 184+ events , every one namespaced tengu — Anthropic's internal codename for Claude Code. tengu feature ok fired 72 times, once per remote feature-flag check. tengu skill loaded fired 48 times. The telemetry shipped to log my question outweighed the question by a factor of thirty thousand. To be fair: I grepped every event — my actual words appear in none of them. I went looking for the config file where I'd opted into all this. It doesn't exist. My settings contain a theme and one hook. My MCP server list is empty in every project. Every fat tool, every schema, every deferred-tool stub is switched on by ~370 server-side feature flags with names like tengu velvet hammer opus 4 8 and tengu amber moleskin , cached to my disk and re-synced whenever the mothership pleases. There's even a complete 5KB onboarding-guide prompt template embedded inside the flag cache , sitting on my SSD in case a feature I've never used ever wants it. There are two kill switches in that file: tengu-off-switch and tengu-fable-off-switch . Neither is mine to flip. Here's the part that annoys me most: it's not even stupid. The 100KB of scaffolding is prompt-cached — I pay full prefill once per session, then it's cheap cache reads. The deferred-tool list I mocked exists precisely to avoid sending 60 more schemas. The 8.7 seconds is a one-time tax; turn two is fast. Every individual decision is defensible. The system is locally rational and globally deranged — a machine that ships a library with every postcard and has optimized the shipping. But step back. A human answering "who are you" needs zero context refresh, no tool manifest, no telemetry burst, no second brain generating a title. We've built agents that must be handed their entire identity, capabilities, safety rules, and worldview from scratch on every request, because the underlying model remembers nothing. Statelessness is the original sin; everything else — the caching, the deferral, the 40-call self-measurement — is increasingly elaborate penance. 19 bytes in. 118,693 bytes of context, 600KB of telemetry, 43 API calls, two models, and 8.7 seconds later: "I'm Claude Code." I know. I read the packet capture.