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The Clevername MCP server costs 12,183 tokens before the first call.

Connect Clevername and its 67 tool definitions are loaded into the model's context on every request — 6.1% of a 200k window spent before your agent does anything.

QUICK ANSWER The Clevername MCP server's tool definitions consume 12,183 tokens — 6.4× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 67 tools · 12,183 tokens · 6.1% of 200k · 1.2% 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 6.1%
1M WINDOW 1.2%

Corpus context: Clevername ranks #130 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 12,183 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
guard_onboard_agent Destructive 4,427 36.3%
guard_register_agent Write 557 4.6%
chat_complete Write 345 2.8%
upsert_mcp_credential Write 275 2.3%
query_logs Read 254 2.1%
guard_revoke_token Destructive 244 2.0%
guard_get_profile Read 232 1.9%
aithroyz_create_tenant_plan Write 216 1.8%
sync_from_claude_config Destructive 199 1.6%
add_mcp_connection Write 195 1.6%
ask_llm Read 193 1.6%
agent__setup Write 188 1.5%
guard_list_agents Read 180 1.5%
ask_consensus Read 172 1.4%
ask_claude Read 171 1.4%
ask_gpt Read 166 1.4%
ask_gemini Read 165 1.4%
agent__add_dependency Write 163 1.3%
get_audit_logs Read 161 1.3%
create_memory Write 160 1.3%
route_plan Write 152 1.2%
call_mcp_tool Execute 144 1.2%
guard_delete_agent Destructive 140 1.1%
get_telemetry_summary Read 138 1.1%
ask_best Read 122 1.0%
get_errors Read 116 1.0%
set_tool_groups Write 113 0.9%
list_available_mcps Read 106 0.9%
update_project Write 106 0.9%
search_memories Read 101 0.8%
list_memories Read 98 0.8%
log_stats Read 92 0.8%
aithroyz_provision_tenant Execute 90 0.7%
vault_sync_status Read 87 0.7%
delete_mcp_credential Destructive 83 0.7%
activate_mcp Write 83 0.7%
vault_sync Execute 81 0.7%
get_telemetry_costs Read 81 0.7%
agent__remove_dependency Destructive 80 0.7%
clevername_login Write 78 0.6%
refresh_tools Read 75 0.6%
vault_preview Read 74 0.6%
list_tool_groups Read 73 0.6%
export_config Write 73 0.6%
aithroyz_get_tenant Read 69 0.6%
agent__activate Write 65 0.5%
create_project Write 60 0.5%
aithroyz_destroy_tenant Destructive 58 0.5%
deactivate_mcp Read 58 0.5%
ping_mcp_connection Read 58 0.5%
agent__list_dependencies Read 55 0.5%
list_mcp_connections Read 52 0.4%
delete_mcp_connection Destructive 49 0.4%
list_mcp_tools Read 49 0.4%
delete_memory Destructive 48 0.4%
list_log_services Read 48 0.4%
agent__list_members Read 47 0.4%
delete_project Destructive 46 0.4%
list_models Read 46 0.4%
agent__list_departments Read 45 0.4%
get_identity Read 45 0.4%
get_project Read 45 0.4%
list_mcp_credentials Read 43 0.4%
aithroyz_list_tenants Read 42 0.3%
health_check Read 37 0.3%
list_active_mcps Read 37 0.3%
list_projects Read 32 0.3%

Most agents use a handful of these tools. They pay for all 67.

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 67 tools (no gateway) 12,183 tokens
3 granted tools ~546 tokens −96%
5 granted tools ~909 tokens −93%
10 granted tools ~1,818 tokens −85%

Clevername token-cost questions.

How many tokens does the Clevername MCP server use?+

Its 67 tool definitions total 12,183 tokens — 6.1% 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 Clevername 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 Clevername's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Clevername 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 546 tokens, a 96% 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 67 catalogued Clevername tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Clevername 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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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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