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The Ljit Mcp Sfmc MCP server costs 7,293 tokens before the first call.

Connect Ljit Mcp Sfmc and its 40 tool definitions are loaded into the model's context on every request — 3.6% of a 200k window spent before your agent does anything.

QUICK ANSWER The Ljit Mcp Sfmc MCP server's tool definitions consume 7,293 tokens — 3.8× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 40 tools · 7,293 tokens · 3.6% of 200k · 0.7% 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 3.6%
1M WINDOW 0.7%

Corpus context: Ljit Mcp Sfmc ranks #862 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 7,293 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 182 tokens per tool.

ToolCategoryTokens% of server
de_create_schema Write 450 6.2%
txn_send_email Write 399 5.5%
txn_send_test_email Write 358 4.9%
de_create Write 353 4.8%
txn_send_email_and_check Write 331 4.5%
txn_preflight_email Execute 330 4.5%
de_update_schema Write 286 3.9%
jrn_list Read 234 3.2%
txn_create_sms_definition Write 224 3.1%
txn_create_push_definition Write 222 3.0%
txn_send_sms Write 217 3.0%
cb_create_asset Write 216 3.0%
txn_create_email_definition Write 215 2.9%
txn_list_definitions Read 211 2.9%
txn_send_push Write 211 2.9%
txn_send_email_batch Write 207 2.8%
txn_send_sms_batch Write 205 2.8%
de_list Read 174 2.4%
de_list_rows Read 173 2.4%
cb_update_asset Write 172 2.4%
txn_inspect_email_definition Read 164 2.2%
txn_validate_email_attributes Read 155 2.1%
cb_list_assets Read 150 2.1%
de_upsert_rows Write 132 1.8%
jrn_resolve_entry_de Write 127 1.7%
txn_update_definition Write 123 1.7%
jrn_get Read 120 1.6%
de_create_folder_soap Write 115 1.6%
txn_get_definition Read 104 1.4%
txn_get_message_status Read 102 1.4%
txn_delete_definition Destructive 96 1.3%
cb_create_folder Write 91 1.2%
de_list_folders_soap Read 83 1.1%
de_delete_schema Destructive 81 1.1%
cb_list_folders Read 81 1.1%
cb_get_asset Read 78 1.1%
de_get_info Read 78 1.1%
jrn_get_event_definition_by_key Read 77 1.1%
jrn_get_event_definition_by_id Read 76 1.0%
cb_delete_asset Destructive 72 1.0%

Most agents use a handful of these tools. They pay for all 40.

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 (182 tokens each).

Grant scopeDefinition costReduction
All 40 tools (no gateway) 7,293 tokens
3 granted tools ~547 tokens −93%
5 granted tools ~912 tokens −88%
10 granted tools ~1,823 tokens −75%

Ljit Mcp Sfmc token-cost questions.

How many tokens does the Ljit Mcp Sfmc MCP server use?+

Its 40 tool definitions total 7,293 tokens — 3.6% 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 Ljit Mcp Sfmc 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 Ljit Mcp Sfmc's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Ljit Mcp Sfmc 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 547 tokens, a 93% 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 40 catalogued Ljit Mcp Sfmc tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Ljit Mcp Sfmc 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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