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The Infranodus MCP server costs 13,369 tokens before the first call.

Connect Infranodus and its 28 tool definitions are loaded into the model's context on every request — 6.7% of a 200k window spent before your agent does anything.

QUICK ANSWER The Infranodus MCP server's tool definitions consume 13,369 tokens — 7.0× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 28 tools · 13,369 tokens · 6.7% of 200k · 1.3% 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 6.7%
1M WINDOW 1.3%

Corpus context: Infranodus ranks #112 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 13,369 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 477 tokens per tool.

ToolCategoryTokens% of server
generate_research_ideas Write 755 5.6%
generate_seo_report Write 662 5.0%
generate_research_questions Write 646 4.8%
optimize_text_structure Read 614 4.6%
develop_text_tool Read 599 4.5%
difference_between_texts Read 590 4.4%
analyze_related_search_queries Read 587 4.4%
merged_graph_from_texts Execute 586 4.4%
develop_conceptual_bridges Read 584 4.4%
develop_latent_topics Read 578 4.3%
overlap_between_texts Read 566 4.2%
generate_responses_from_graph Write 565 4.2%
list_graphs Read 535 4.0%
analyze_google_search_results Read 529 4.0%
memory_add_relations Write 519 3.9%
search_queries_vs_search_results Read 498 3.7%
generate_knowledge_graph Write 481 3.6%
create_knowledge_graph Write 442 3.3%
analyze_text Read 441 3.3%
analyze_existing_graph_by_name Read 422 3.2%
retrieve_from_knowledge_base Read 380 2.8%
generate_contextual_hint Write 348 2.6%
generate_topical_clusters Write 347 2.6%
generate_content_gaps Write 299 2.2%
search Read 268 2.0%
memory_get_relations Read 265 2.0%
fetch Read 192 1.4%
get_more_tools Read 71 0.5%

Most agents use a handful of these tools. They pay for all 28.

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 (477 tokens each).

Grant scopeDefinition costReduction
All 28 tools (no gateway) 13,369 tokens
3 granted tools ~1,432 tokens −89%
5 granted tools ~2,387 tokens −82%
10 granted tools ~4,775 tokens −64%

Infranodus token-cost questions.

How many tokens does the Infranodus MCP server use?+

Its 28 tool definitions total 13,369 tokens — 6.7% 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 Infranodus 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 Infranodus's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Infranodus 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 1,432 tokens, a 89% 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 28 catalogued Infranodus tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Infranodus 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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