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The Agent Team MCP server costs 3,657 tokens before the first call.

Connect Agent Team and its 46 tool definitions are loaded into the model's context on every request — 1.8% of a 200k window spent before your agent does anything.

QUICK ANSWER The Agent Team MCP server's tool definitions consume 3,657 tokens — around the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 46 tools · 3,657 tokens · 1.8% of 200k · 0.4% 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 1.8%
1M WINDOW 0.4%

Corpus context: Agent Team ranks #1194 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 3,657 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 80 tokens per tool.

ToolCategoryTokens% of server
share_artifact Read 191 5.2%
get_agent_prompt Read 154 4.2%
ask_user_question Read 108 3.0%
request_team_expansion Read 108 3.0%
get_orchestration_instructions Read 103 2.8%
log_journal_entry Read 101 2.8%
log_decision Read 98 2.7%
create_discussion Write 98 2.7%
log_work Read 94 2.6%
resolve_expansion_request Write 90 2.5%
add_discussion_message Write 89 2.4%
create_task Write 86 2.4%
update_task Write 86 2.4%
list_tasks Read 81 2.2%
list_user_questions Read 81 2.2%
list_expansion_requests Read 80 2.2%
add_task_comment Write 79 2.2%
update_artifact Write 78 2.1%
delete_project Destructive 77 2.1%
update_project_status Write 77 2.1%
get_work_history Read 74 2.0%
list_my_comments Read 74 2.0%
get_my_work Read 73 2.0%
add_discussion_participant Write 73 2.0%
list_artifacts Read 72 2.0%
list_team_members Read 72 2.0%
add_team_member Write 72 2.0%
list_discussions Read 71 1.9%
answer_user_question Write 71 1.9%
update_project_summary Write 71 1.9%
list_journal_entries Read 70 1.9%
update_discussion_summary Write 69 1.9%
create_project Write 68 1.9%
remove_team_member Destructive 64 1.8%
get_discussion Read 61 1.7%
get_summary_version Read 61 1.7%
get_project_summary Read 60 1.6%
get_team_protocol Read 60 1.6%
list_summary_history Read 60 1.6%
get_artifact Read 59 1.6%
list_decisions Read 59 1.6%
list_task_comments Read 59 1.6%
get_decision Read 58 1.6%
get_project Read 57 1.6%
get_task Read 57 1.6%
list_projects Read 53 1.4%

Most agents use a handful of these tools. They pay for all 46.

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 (80 tokens each).

Grant scopeDefinition costReduction
All 46 tools (no gateway) 3,657 tokens
3 granted tools ~239 tokens −93%
5 granted tools ~398 tokens −89%
10 granted tools ~795 tokens −78%

Agent Team token-cost questions.

How many tokens does the Agent Team MCP server use?+

Its 46 tool definitions total 3,657 tokens — 1.8% 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 Agent Team 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 Agent Team's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Agent Team 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 239 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 46 catalogued Agent Team tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Agent Team 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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