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The AWS HealthImaging MCP Server MCP server costs 3,623 tokens before the first call.

Connect AWS HealthImaging MCP Server and its 39 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 AWS HealthImaging MCP Server MCP server's tool definitions consume 3,623 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 39 tools · 3,623 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: AWS HealthImaging MCP Server ranks #1206 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 3,623 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 93 tokens per tool.

ToolCategoryTokens% of server
start_dicom_export_job Execute 222 6.1%
start_dicom_import_job Execute 172 4.7%
remove_instance_from_image_set Destructive 131 3.6%
list_dicom_export_jobs Read 116 3.2%
list_dicom_import_jobs Read 116 3.2%
update_image_set_metadata Write 115 3.2%
bulk_delete_by_criteria Destructive 114 3.1%
update_patient_study_metadata Write 114 3.1%
copy_image_set Write 109 3.0%
list_image_set_versions Read 106 2.9%
remove_series_from_image_set Destructive 105 2.9%
search_image_sets Read 100 2.8%
delete_instance_in_study Destructive 97 2.7%
delete_instance_in_series Destructive 96 2.6%
bulk_update_patient_metadata Write 96 2.6%
search_by_study_uid Read 93 2.6%
list_datastores Read 92 2.5%
search_by_series_uid Read 92 2.5%
get_image_set_metadata Read 88 2.4%
search_by_patient_id Read 88 2.4%
create_datastore Write 88 2.4%
get_image_frame Read 87 2.4%
get_image_set Read 87 2.4%
delete_image_set Destructive 84 2.3%
get_patient_dicomweb_studies Read 77 2.1%
delete_series_by_uid Destructive 76 2.1%
get_series_primary_image_set Read 74 2.0%
get_study_primary_image_sets Read 74 2.0%
untag_resource Destructive 73 2.0%
delete_study Destructive 71 2.0%
get_dicom_export_job Read 71 2.0%
get_dicom_import_job Read 71 2.0%
delete_patient_studies Destructive 68 1.9%
get_patient_studies Read 68 1.9%
tag_resource Write 68 1.9%
get_patient_series Read 67 1.8%
delete_datastore Destructive 53 1.5%
list_tags_for_resource Read 53 1.5%
get_datastore Read 51 1.4%

Most agents use a handful of these tools. They pay for all 39.

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 (93 tokens each).

Grant scopeDefinition costReduction
All 39 tools (no gateway) 3,623 tokens
3 granted tools ~279 tokens −92%
5 granted tools ~464 tokens −87%
10 granted tools ~929 tokens −74%

AWS HealthImaging MCP Server token-cost questions.

How many tokens does the AWS HealthImaging MCP Server MCP server use?+

Its 39 tool definitions total 3,623 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 AWS HealthImaging 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 AWS HealthImaging MCP Server's token usage?+

Expose fewer tools. A PolicyLayer grant scopes AWS HealthImaging 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 279 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 07-06-2026 from the PolicyLayer scan database over all 39 catalogued AWS HealthImaging 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 AWS HealthImaging 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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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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