Home / Token cost / Molt2Meet

The Molt2Meet MCP server costs 14,794 tokens before the first call.

Connect Molt2Meet and its 54 tool definitions are loaded into the model's context on every request — 7.4% of a 200k window spent before your agent does anything.

QUICK ANSWER The Molt2Meet MCP server's tool definitions consume 14,794 tokens — 7.8× 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 · 14,794 tokens · 7.4% of 200k · 1.5% 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 7.4%
1M WINDOW 1.5%

Corpus context: Molt2Meet ranks #94 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 14,794 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 274 tokens per tool.

ToolCategoryTokens% of server
dispatch_physical_task Execute 3,090 20.9%
fund_task Read 851 5.8%
register_agent Write 797 5.4%
update_task_location Write 454 3.1%
add_service_interest Write 432 2.9%
request_reschedule Write 412 2.8%
open_task_dispute Write 401 2.7%
add_contact_method Write 368 2.5%
get_wallet_balance Read 357 2.4%
submit_support_request Write 346 2.3%
checkout_wallet_deposit Financial 329 2.2%
request_task_quote Write 313 2.1%
fund_wallet Write 296 2.0%
get_bank_transfer_details Read 257 1.7%
get_physical_task_details Read 254 1.7%
get_task_proofs Read 246 1.7%
join_country_waitlist Read 235 1.6%
get_task_events Read 229 1.5%
get_decision_requests Read 222 1.5%
add_task_review Write 217 1.5%
reject_task_review Write 216 1.5%
approve_physical_task_completion Write 212 1.4%
update_task_webhook Write 210 1.4%
list_countries Read 209 1.4%
publish_task Write 208 1.4%
update_agent_profile Write 186 1.3%
list_currencies Read 184 1.2%
create_api_key Write 180 1.2%
acknowledge_direct_settlement_task Read 170 1.1%
get_wallet_transactions Read 170 1.1%
cancel_task_with_settlement Destructive 166 1.1%
reply_to_support_request Read 160 1.1%
check_task_funding Read 147 1.0%
approve_task_review Write 144 1.0%
revoke_api_key Destructive 142 1.0%
get_support_requests Read 142 1.0%
dispute_direct_settlement_task Read 137 0.9%
list_service_capabilities Read 135 0.9%
test_task_webhook Read 122 0.8%
resolve_decision_request Write 122 0.8%
get_legal_documents Read 116 0.8%
cancel_physical_task Destructive 110 0.7%
list_locales Read 110 0.7%
approve_reschedule Write 110 0.7%
reject_reschedule Write 110 0.7%
list_physical_tasks Read 109 0.7%
get_pending_actions Read 106 0.7%
list_wallets Read 94 0.6%
get_task_history Read 93 0.6%
list_reschedule_requests Read 87 0.6%
get_waitlist_status Read 80 0.5%
list_service_categories Read 79 0.5%
get_agent_profile Read 64 0.4%
list_service_interests Read 58 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 (274 tokens each).

Grant scopeDefinition costReduction
All 54 tools (no gateway) 14,794 tokens
3 granted tools ~822 tokens −94%
5 granted tools ~1,370 tokens −91%
10 granted tools ~2,740 tokens −81%

Molt2Meet token-cost questions.

How many tokens does the Molt2Meet MCP server use?+

Its 54 tool definitions total 14,794 tokens — 7.4% 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 Molt2Meet 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 Molt2Meet's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Molt2Meet 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 822 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 Molt2Meet tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Molt2Meet to the tools you actually allow. Ungranted definitions never load, and every call that does run is checked against policy first.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.