MCP definitions use 10.6× more input tokens until tool search defers them

Evidence status

Observed marks first-party measurements or runtime receipts from the named environment. Derived marks arithmetic calculated from cited inputs. Specified marks vendor or standards documentation. Implemented and deployed name code and live-state evidence, respectively. Reproduced means the stated procedure was rerun. Externally attested marks operator reports; those reports show that an experience occurred, not that it is universal.

A definition is a name, a sentence and a schema, and the schema is the part that grows

One live tool object, from POST https://miscsubjects.com/api/mcp with {"jsonrpc":"2.0","id":1,"method":"tools/list"}:

{
  "name": "CF_AI_GATEWAY_LIST_LOGS",
  "description": "List Logs MCP: https://ai-gateway.mcp.cloudflare.com/sse [fn · cf_ai_gateway]",
  "inputSchema": {
    "type": "object",
    "properties": {
      "gateway_id": { "type": "string", "description": "The gateway ID." },
      "page": { "default": 1, "type": "integer" },
      "per_page": { "default": 20, "type": "integer" },
      "order_by": { "default": "created_at", "type": "string",
        "enum": ["created_at","provider","model","model_type","success",
                 "cached","cost","tokens_in","tokens_out","duration","feedback"] },
      "order_by_direction": { "default": "desc", "type": "string", "enum": ["asc","desc"] },
      "start_date": { "type": "string" },
      "end_date": { "type": "string" },
      "feedback": { "type": "number" },
      "success": { "type": "boolean" },
      "cached": { "type": "boolean" },
      "model": { "type": "string" },
      "provider": { "type": "string" }
    },
    "required": ["gateway_id"]
  }
}

That object is 264 tokens by the o200k_base tokenizer: 11 for the name, 22 for the description, 231 for argument names, types, defaults and one enum of eleven strings.

A description is written once by a human and stays a sentence. A schema grows with the API behind it — every optional filter, enum member and nested object — and nothing prunes it. The MCP specification requires it: tools/list returns name, optional title, description and inputSchema per tool, so there is no conforming way to publish a tool without publishing its argument surface.

Across the whole 831-tool catalogue on 2026-07-26, names cost 4,064 tokens, descriptions 54,596, schemas 54,465. Level in aggregate, which is not the folklore. Split by size and the folklore returns:

Cheap tools are mostly prose; expensive tools are almost entirely schema. A catalogue's bill is set by its handful of query-shaped tools, not its median row. An independent measurement on a different server found the same tail: G-Core's MCP server at GCORE_TOOLS=* advertises 741 tools for about 488,013 tokens, an average of 659 per tool, one tool alone at about 7,046 tokens of schema.

Measure it three ways, cheapest first

1. /context, in a running session. Breaks the window into system prompt, system tools, MCP tools, memory, skills and messages. Free, one second. Read the contradiction section before trusting a zero.

2. The usage field of any API response. Every Messages API response carries usage.input_tokens, usage.output_tokens and the cache counters. Send the same one-word prompt twice, with the servers attached and without, and subtract. The only method that measures what you are billed for.

3. A capture server, for what the client puts on the wire. A local HTTP server that speaks enough of the Messages API to answer, and logs every request:

git clone https://github.com/massoumicyrus/claude-code-cloudflare-gateway
cd claude-code-cloudflare-gateway
node tools/capture-gateway.mjs      # listens on :8787, appends capture.jsonl

It records n_tools, tool_names, system_chars, system_cache_control, metadata and the model per request, redacting authorization and x-api-key. Point the client at it twice, changing one variable:

ANTHROPIC_BASE_URL=http://localhost:8787 ANTHROPIC_AUTH_TOKEN=x \
  ENABLE_TOOL_SEARCH=false claude -p "say ok"

ANTHROPIC_BASE_URL=http://localhost:8787 ANTHROPIC_AUTH_TOKEN=x \
  ENABLE_TOOL_SEARCH=true claude -p "say ok"

Read the two tool counts out of the log:

python3 -c "
import json
for line in open('capture.jsonl'):
    r = json.loads(line)
    if r.get('n_tools') is not None:
        print(r['model'], 'tools=', r['n_tools'])
"

Two lines. On claude-cli 2.1.165 against that server on 2026-07-25: tools= 856 and tools= 9 — the nine being Agent, AskUserQuestion, Bash, Edit, Read, Skill, ToolSearch, Workflow, Write.

To price a catalogue without running the client, count the tokens the server publishes. The command behind the 831-tool figures above, good against any HTTP MCP server that answers tools/list:

curl -s -X POST https://miscsubjects.com/api/mcp \
  -H "Authorization: Bearer $MCP_TOKEN" -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' -o tools.json

python3 -m venv /tmp/tokvenv && /tmp/tokvenv/bin/pip install tiktoken
/tmp/tokvenv/bin/python -c "
import json, tiktoken
enc = tiktoken.get_encoding('o200k_base')
t = json.load(open('tools.json'))['result']['tools']
tot = sum(len(enc.encode(json.dumps(x))) for x in t)
print('tools', len(t), 'tokens', tot, 'mean', round(tot/len(t), 1))
"

Output on 2026-07-26: tools 831 tokens 127440 mean 153.4, from a 434,636-byte response. o200k_base is OpenAI's tokenizer, not Anthropic's — treat it as a close estimate and billed usage as the truth. They agree to about 12% here: 149,187 billed input tokens for 856 definitions plus a system prompt and a short user message is 174.3 tokens per definition against the tokenizer's 153.4.

Three configurations, same catalogue, same day

Same machine, same catalogue of 891 capabilities, same trivial prompt, @cf/moonshotai/kimi-k2.7-code through a Cloudflare AI Gateway, 2026-07-25. Tokens and cost read from the gateway's own log rows.

149,187 input tokens is 74.6% of a 200,000-token window, spent before the user's sentence is read. Deferring the definitions cuts that 10.6-fold, 135,078 tokens a turn.

The third row decides architecture: tool search on is as cheap as having no MCP server at all — 14,109 against 14,071, a 0.3% difference — with every tool still reachable. The tools were never the cost. The definitions were.

The dollar column is the gateway's own accounting and is an estimate; the token counts are measured at both ends and the argument rests on them.

That 14,071-token row is still readable. Fetched 2026-07-26 from the AI Gateway logs REST endpoint (GET /accounts/<account_id>/ai-gateway/gateways/default/logs?per_page=3&order_by=created_at&order_by_direction=desc):

{"created_at":"2026-07-26T03:43:18.667Z","model":"@cf/moonshotai/kimi-k2.7-code",
 "metadata":{"via":"claude-code","shim":"api/aig","tools":9},
 "tokens_in":14071,"tokens_out":170,"cost":0.00456265,
 "usage_metadata":{"input_cached_tokens":12480}}

metadata.tools is what the client sent: nine, not 856, with the same catalogue attached.

The attribution line was worth more than 12,000 cached tokens a turn

Claude Code prepends an attribution block — client version and prompt fingerprint — to the start of the system prompt. A prompt cache keys on an exact prefix, so a value that changes per request, at the front, means the prefix never matches and nothing before it is ever a hit. Anthropic's environment-variable reference: set CLAUDE_CODE_ATTRIBUTION_HEADER to 0 to omit the block, "Disabling it improves prompt-cache hit rates when routing through an LLM gateway. Caching on a direct connection to the Anthropic API is unaffected either way."

Measured on the rows above, cached input went from 64 tokens to 12,480 tokens per turn once the line was dropped — 88% of a 14,109-token turn arriving from cache instead of priced as fresh.

export CLAUDE_CODE_ATTRIBUTION_HEADER=0

Anthropic's gateway protocol reference adds the version detail and the right place to fix it: from v2.1.181 the block is stable for the lifetime of a conversation behind a custom base URL, and "If your gateway must reshape system content, set CLAUDE_CODE_ATTRIBUTION_HEADER=0 so Claude Code omits the block… omit it at the client rather than stripping or moving it in the gateway." Stripping it gateway-side breaks attribution downstream; omitting it at the client does not.

Deferred tool search: names stay, schemas arrive on request

Anthropic's documentation describes the mechanism: "When tool search is active, tool definitions are withheld from the context window. The agent receives a summary of available tools and searches for relevant ones when the task requires a capability not already loaded. Up to five of the most relevant tools are loaded into context by default." A discovered tool stays available for later turns; if compaction removes it, the model searches again.

The switch:

export ENABLE_TOOL_SEARCH=true

Permanently, in ~/.claude/settings.json:

{ "env": { "ENABLE_TOOL_SEARCH": "true", "CLAUDE_CODE_ATTRIBUTION_HEADER": "0" } }

Every value the variable accepts:

Two costs come with it, both small. The search tool's own definition stays in the request — it is one of the nine measured above, and Anthropic's API reference is explicit that "At least one tool, normally the tool search tool itself, must stay non-deferred." And the first use of an unseen tool costs an extra round-trip; below roughly ten tools, loading everything upfront is faster.

The API-side mechanism differs from the client-side one in a way that matters if you are building a gateway. With defer_loading: true on the Messages API, "You still send every tool's full definition in the tools array on every request, including the deferred ones. The API needs them server-side to run the search." The saving there is context, not bytes. The capture above shows the client doing the other thing — sending nine definitions — because behind a non-first-party base URL Claude Code resolves the search itself. Both are called tool search. Only one shrinks what leaves your machine.

Five ways the definitions stay in the bill anyway

The cache row cuts the other way for the deferred case. Anthropic's caching page puts tool definitions in the system-prompt layer, so the cache invalidates when the set of definitions changes between turns — but with deferred tools, "a server connecting, disconnecting, or changing its tool list only appends new content and doesn't disturb anything already cached", while with tools in the prefix "any change to them invalidates the cache". A gateway is named as one place where deferral is off and the prefix is therefore fragile: a stdio process exiting, an HTTP session expiring or an automatic reconnect invalidates the whole cached prefix with nobody touching a keyboard.

An operator who ran it and was not convinced, on Hacker News on 2026-03-15: "And no, the tool search function recently introduced by Anthropic does not completely solve this problem."

One harness reports zero tokens per tool. Others publish tables of tens of thousands

Zero, filed against Claude Code as issue 23228 on 2026-02-05: "When running the /context command to check token usage, the output includes a long list of all MCP tool definitions (28 in my case), each showing '0 tokens'."

Not zero, filed on 2026-03-01 with a per-server breakdown: "The main agent context burns 16.9k tokens (8.5% of 200k) loading 68 MCP tool schemas at session start. Most are never called by the main agent — they're used by subagents." That author's table attributes 6.0k to one server's 25 tools, 3.3k to another's 14. Two more, independently: 741 tools at about 488,013 tokens on a G-Core server, and 250-plus definitions across 9 servers at "~40,000-70,000 tokens of tool definitions loaded upfront".

Both can be true, and the mechanism is documented. /context reports what is in the context window, category by category. Deferred definitions are by construction not in the window — Anthropic's cost guidance: "MCP tool definitions are deferred by default, so only tool names enter context until Claude uses a specific tool." A per-tool row of 0 tokens is then accurate, and the residual cost lands under another heading. The tables of tens of thousands come from sessions where the definitions were not deferred — an older client, a false setting, a non-first-party base URL, alwaysLoad, an exempt built-in server — and from harnesses that count the catalogue rather than ask the client.

Which to trust for a decision: neither, over the usage field of your own responses. /context reports placement, not billing, and its zero is silent about the 3,900 tokens of exempt built-in schemas in issue 76372. A tokenizer table reports the size of a catalogue, not what your client chose to send — the capture above shows a client sending 9 of 856. Only usage.input_tokens, differenced across two otherwise identical runs, answers the question being asked.

One dissent earns its place, from Hacker News on 2026-05-30: "The idea that MCP tool definitions take up a certain number of tokens is laughable. That's an implementation detail of the agent harness." Correct about the protocol, beside the point about the invoice: the protocol mandates no loading strategy, and the client you are running has already picked one and is billing you for it.

The arithmetic at 200 turns a day

200 model turns in a working day, 30 days in a month. Substitute your own turn count; the multiplication is the same.

One environment variable is worth $4.82 a day and $144.54 a month on one machine, one catalogue, these rates. In tokens, which do not depend on anyone's pricing: 135,078 fewer input tokens per turn, 27,015,600 fewer per day.

Two effects the table does not price. The definitions occupied 74.6% of a 200,000-token window, so the ceiling on what a session can hold moves further than the invoice does. And tool-selection accuracy is documented to degrade "with more than 30-50 tools loaded at once" — a quality cost with no line item.

Five responses, ranked by what they cost you to adopt

1. Turn tool search on. One variable, no code, every tool still reachable, measured 10.6× fewer input tokens. Trade-off: an extra round-trip on first use of an unseen tool; unavailable behind a base URL that cannot forward tool_reference blocks; below ten tools it is slower than loading them.

2. Scope servers per project. Enable a server only in the repository that needs it. An operator on Hacker News, 2026-07-12: "I enable tools specific to each project only in that project, and have very very few in my global config. Like <5k tokens worth." Trade-off: manual, and it fails the way manual hygiene always fails — the day you forget.

3. Collapse many tools into few. Replace N tools with one or two that take a name and arguments and dispatch internally. From the operator who built it: "This basically takes your APIs, databases, and docs and compresses them into 2 MCP tools (~1,000 tokens) instead of N tools (100K+ tokens)." A minimal version of the same idea, reported at about 60 tokens: one bare tool with a one-line description that unlocks the rest when called. Trade-off: you have rebuilt tool search without the harness's search quality, and the model must learn your dispatch convention.

4. Take the catalogue out of the prompt entirely. Publish capabilities behind an HTTP contract the model reads on demand, keep only the built-in tools. Measured at 14,071 input tokens with 891 capabilities reachable — the same number as tool search, a different structure, because catalogue size is no longer a term in the per-turn equation and no host support is required. Trade-off: your capabilities are not MCP tools, so anything that consumes MCP does not see them. Dimension by dimension in Tool search versus a catalogue as data, the data layer in Tooling as data, the three exposures side by side in MCP as a projection.

5. Do nothing. Defensible at a small tool count: under about ten tools, upfront loading is faster and the search round-trip is pure overhead. At 150–200 tools, two reports show the limit: one one-word prompt produced a request with "prompt_tokens: 154,367"; in the other, roughly 200 tool schemas exceeded a 32k window so completely that "/compact succeeded in producing a summary, but the very next request still failed."

Symptom, cause, fix

The gateway that produced these log rows, and how the same client runs on a non-Anthropic model, is documented in Claude Code on Kimi, GLM or Grok through your own Cloudflare account.