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The Amazon Data Processing MCP Server MCP server costs 27,284 tokens before the first call.

Connect Amazon Data Processing MCP Server and its 36 tool definitions are loaded into the model's context on every request — 14% of a 200k window spent before your agent does anything.

QUICK ANSWER The Amazon Data Processing MCP Server MCP server's tool definitions consume 27,284 tokens — 25× the median MCP server (1,075 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 36 tools · 27,284 tokens · 14% of 200k · 2.7% 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 14%
1M WINDOW 2.7%

Corpus context: Amazon Data Processing MCP Server ranks #14 of 1,659 measured MCP servers by definition cost. The median is 1,075 tokens, p90 is 6,119, and the heaviest (Fusionauth) is 183,337 — 92% of a 200k window on its own.

Where the 27,284 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 758 tokens per tool.

ToolCategoryTokens% of server
manage_aws_emr_clusters Write 2,561 9.4%
manage_aws_emr_serverless_job_runs Write 1,516 5.6%
manage_aws_glue_jobs Write 1,468 5.4%
manage_aws_emr_ec2_instances Write 1,426 5.2%
manage_aws_emr_serverless_applications Write 1,288 4.7%
manage_aws_glue_sessions Write 1,019 3.7%
manage_aws_athena_query_executions Write 993 3.6%
manage_aws_athena_data_catalogs Write 925 3.4%
manage_aws_emr_ec2_steps Write 885 3.2%
manage_aws_athena_named_queries Write 884 3.2%
manage_aws_glue_crawlers Write 822 3.0%
manage_aws_glue_connection_metadata Write 821 3.0%
manage_aws_glue_triggers Write 798 2.9%
add_inline_policy Write 752 2.8%
manage_aws_glue_connections Write 722 2.6%
manage_aws_athena_databases_and_tables Write 641 2.3%
manage_aws_glue_databases Write 640 2.3%
manage_aws_glue_partitions Write 634 2.3%
manage_aws_athena_workgroups Write 632 2.3%
manage_aws_glue_workflows Write 614 2.3%
manage_aws_glue_tables Write 596 2.2%
manage_aws_glue_classifiers Write 570 2.1%
manage_aws_glue_crawler_management Write 570 2.1%
manage_aws_glue_statements Write 562 2.1%
manage_aws_glue_usage_profiles Write 548 2.0%
manage_aws_glue_catalog Write 531 1.9%
create_data_processing_role Write 525 1.9%
manage_aws_glue_resource_policies Write 515 1.9%
manage_aws_glue_security_configurations Write 497 1.8%
manage_aws_glue_encryption Write 496 1.8%
get_roles_for_service Read 381 1.4%
manage_aws_glue_connection_types Write 366 1.3%
upload_to_s3 Write 364 1.3%
get_policies_for_role Read 334 1.2%
list_s3_buckets Read 253 0.9%
analyze_s3_usage_for_data_processing Read 135 0.5%

Most agents use a handful of these tools. They pay for all 36.

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 (758 tokens each).

Grant scopeDefinition costReduction
All 36 tools (no gateway) 27,284 tokens
3 granted tools ~2,274 tokens −92%
5 granted tools ~3,789 tokens −86%
10 granted tools ~7,579 tokens −72%

Amazon Data Processing MCP Server token-cost questions.

How many tokens does the Amazon Data Processing MCP Server MCP server use?+

Its 36 tool definitions total 27,284 tokens — 14% 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 Amazon Data Processing MCP Server 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 Amazon Data Processing MCP Server's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Amazon Data Processing MCP Server 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 2,274 tokens, a 92% 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 05-06-2026 from the PolicyLayer scan database over all 36 catalogued Amazon Data Processing MCP Server tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Amazon Data Processing MCP Server 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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