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The TunnelMind Data API MCP server costs 18,740 tokens before the first call.

Connect TunnelMind Data API and its 54 tool definitions are loaded into the model's context on every request — 9.4% of a 200k window spent before your agent does anything.

QUICK ANSWER The TunnelMind Data API MCP server's tool definitions consume 18,740 tokens — 9.8× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 54 tools · 18,740 tokens · 9.4% of 200k · 1.9% of 1M Method →

What that buys before your agent starts working.

Tool definitions are overhead: they occupy context on every request and compete with your code, documents and conversation history for the same window.

200K WINDOW 9.4%
1M WINDOW 1.9%

Corpus context: TunnelMind Data API ranks #59 of 3,213 measured MCP servers by definition cost. The median is 1,905 tokens, p90 is 7,952, and the heaviest (Fusionauth) is 183,337 — 92% of a 200k window on its own.

Where the 18,740 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 347 tokens per tool.

ToolCategoryTokens% of server
sigil_verify_ads_txt Read 825 4.4%
get_analyst_config Read 765 4.1%
sigil_verify_supply_path Read 620 3.3%
cross_lens_verify Read 586 3.1%
generate_receipt Write 583 3.1%
preflight_should_i_act Read 579 3.1%
x402_echo Read 558 3.0%
get_domain Read 544 2.9%
sigil_verify_supply_chain Read 540 2.9%
sigil_verify_ads_txt_batch Read 510 2.7%
list_domains Read 487 2.6%
check_receipt_revoked Read 482 2.6%
audit_export Write 482 2.6%
intel_http Write 476 2.5%
profile_entity Read 456 2.4%
verify_receipt Read 429 2.3%
get_bgp_events Read 411 2.2%
intel_robots Read 403 2.2%
list_entities Read 399 2.1%
intel_optout Read 390 2.1%
get_entity Read 387 2.1%
intel_agent Write 385 2.1%
get_receipt Read 383 2.0%
search Read 372 2.0%
intel_inject Write 366 2.0%
intel_stack Read 363 1.9%
get_task Read 359 1.9%
stream_task Read 341 1.8%
sigil_verify_app_bundle Read 324 1.7%
get_api_key Read 290 1.5%
sigil_traverse Write 287 1.5%
explain_verdict Read 280 1.5%
sigil_verify_domain Read 276 1.5%
sigil_score_entity Read 271 1.4%
sigil_atap_witness Read 253 1.4%
revoke_api_key Destructive 248 1.3%
sigil_atap_register_ait Write 239 1.3%
health_check Read 235 1.3%
sigil_verify_ip_type Read 221 1.2%
sigil_ads_txt_history Write 212 1.1%
cancel_task Destructive 210 1.1%
get_stats Read 208 1.1%
sigil_receipt_generate Write 203 1.1%
cross_lens_lookup Read 198 1.1%
sigil_verify_adscert Read 190 1.0%
signal_tracker_density Read 180 1.0%
signal_dark_pool_risk Read 163 0.9%
signal_halo_score Read 154 0.8%
sigil_score_batch Read 149 0.8%
signal_team_signal Read 132 0.7%
sigil_verify_token Read 117 0.6%
sigil_score_weights Read 79 0.4%
sigil_atap_roll_block Read 72 0.4%
sigil_atap_ait_status Read 68 0.4%

Most agents use a handful of these tools. They pay for all 54.

A PolicyLayer grant exposes only the tools you allow — ungranted definitions are filtered out of the tool list, so they never enter the context window. Estimates below assume typical-weight tools (347 tokens each).

Grant scopeDefinition costReduction
All 54 tools (no gateway) 18,740 tokens
3 granted tools ~1,041 tokens −94%
5 granted tools ~1,735 tokens −91%
10 granted tools ~3,470 tokens −81%

TunnelMind Data API token-cost questions.

How many tokens does the TunnelMind Data API MCP server use?+

Its 54 tool definitions total 18,740 tokens — 9.4% of a 200k context window — measured with tiktoken o200k_base over the serialised tools/list payload. Exact counts vary slightly by client and model.

Why does TunnelMind Data API consume tokens before I send a message?+

MCP clients load every connected server's tool definitions — name, description, and input schema — into the model's context so it knows what it can call. That payload is charged against your context window on every request, whether or not a tool is used.

How do I reduce TunnelMind Data API's token usage?+

Expose fewer tools. A PolicyLayer grant scopes TunnelMind Data API to only the tools you allow — ungranted definitions are filtered out of the tool list, so they never enter the context window. A grant of 3 typical tools costs roughly 1,041 tokens, a 94% reduction.

Does deferred tool loading fix this?+

Partially, in some clients. Claude Code defers MCP tool schemas behind a tool-search step by default, and VS Code has experimental grouping — but you still pay tokens per search and reload, and Cursor, Windsurf and Gemini CLI load definitions upfront. Reducing the exposed tool set cuts the cost in every client.

How these numbers were measured.

01
Serialisation

Each tool is serialised as a tools/list entry — name, description, input schema — from the schemas in the PolicyLayer scan database. Clients differ slightly in framing, so treat counts as close estimates.

02
Tokeniser

tiktoken o200k_base (GPT-4o/o-series). Anthropic's current tokeniser isn't published, so Claude's exact counts will differ; for English text and JSON schemas the totals are close enough to treat these as estimates.

03
Deferred loading

Some clients now defer schema loading (Claude Code's tool search; VS Code experimental grouping). You still pay per search and reload — and Cursor, Windsurf and Gemini CLI load everything upfront.

Computed 07-06-2026 from the PolicyLayer scan database over all 54 catalogued TunnelMind Data API tools. Counts refresh with every site build.

Expose only the tools you use — the rest never enter your context.

A PolicyLayer grant scopes TunnelMind Data API to the tools you actually allow. Ungranted definitions never load, and every call that does run is checked against policy first.

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