// MCP TOKEN COST

The AlpineDataWorks Intelligence Server MCP server costs 20,510 tokens before the first call.

Every request your agent makes carries every tool definition this server exposes — context your code, documents and conversation can't use, mostly for tools the agent never calls. You don't need them all in the window, and you don't have to pay for them.

QUICK ANSWER The AlpineDataWorks Intelligence Server MCP server's 196 tool definitions consume 20,510 tokens — 10% of a 200k context window, and 9.6× the median MCP server (2,142 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS tiktoken o200k_base · rank #96 of 5,402 measured servers · refreshed every build Method →

// CONTEXT WINDOW SHARE

What that costs 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 10%

1M WINDOW 2.1%

Corpus context: AlpineDataWorks Intelligence Server ranks #96 of 5,402 measured MCP servers by definition cost. The median is 2,142 tokens, p90 is 11,749, and the heaviest (Ainumbers Mcp Apps) is 161,750 — 81% of a 200k window on its own. New to this? See MCP token cost and context window in the glossary.

Token cost is one axis. See the risk picture across your whole stack →

// PER-TOOL BREAKDOWN

Where the 20,510 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 105 tokens per tool.

Tool	Category	Tokens	% of server
adw.feedback	Write	247	1.2%
adw.county_cancer	Read	176	0.9%
adw.air_quality_risk	Read	172	0.8%
adw.county_sdoh	Read	157	0.8%
adw.county_mortality	Read	150	0.7%
adw.county_water	Read	143	0.7%
adw.obesity_risk	Read	140	0.7%
adw.adw_032	Read	139	0.7%
adw.adw_204	Read	137	0.7%
adw.adw_020	Read	134	0.7%
adw.adw_016	Read	130	0.6%
adw.adw_201	Read	130	0.6%
adw.adw_018	Read	129	0.6%
adw.adw_205	Read	126	0.6%
adw.adw_019	Read	125	0.6%
adw.adw_021	Read	125	0.6%
adw.adw_202	Read	125	0.6%
adw.adw_025	Read	117	0.6%
adw.adw_422	Read	115	0.6%
adw.adw_027	Read	113	0.6%
adw.adw_421	Read	113	0.6%
adw.adw_033	Read	111	0.5%
adw.adw_047	Read	111	0.5%
adw.adw_024	Read	110	0.5%
adw.adw_106	Read	110	0.5%
adw.adw_420	Read	110	0.5%
adw.adw_423	Read	110	0.5%
adw.adw_017	Read	109	0.5%
adw.adw_029	Read	109	0.5%
adw.adw_418	Read	109	0.5%
adw.adw_026	Read	108	0.5%
adw.adw_102	Read	108	0.5%
adw.adw_396	Read	108	0.5%
adw.adw_419	Read	108	0.5%
adw.adw_023	Read	107	0.5%
adw.adw_028	Read	107	0.5%
adw.adw_049	Read	107	0.5%
adw.adw_101	Read	107	0.5%
adw.adw_105	Read	107	0.5%
adw.adw_348	Read	107	0.5%
adw.adw_386	Read	107	0.5%
adw.adw_393	Read	107	0.5%
adw.adw_401	Read	107	0.5%
adw.adw_412	Read	107	0.5%
adw.adw_417	Read	107	0.5%
adw.adw_424	Read	107	0.5%
adw.adw_425	Read	107	0.5%
adw.adw_428	Read	107	0.5%
adw.adw_045	Read	106	0.5%
adw.adw_103	Read	106	0.5%
adw.adw_383	Read	106	0.5%
adw.adw_390	Read	106	0.5%
adw.adw_395	Read	106	0.5%
adw.adw_397	Read	106	0.5%
adw.adw_015	Read	105	0.5%
adw.adw_030	Read	105	0.5%
adw.adw_382	Read	105	0.5%
adw.adw_388	Read	105	0.5%
adw.adw_011	Read	104	0.5%
adw.adw_031	Read	104	0.5%
adw.adw_038	Read	104	0.5%
adw.adw_044	Read	104	0.5%
adw.adw_104	Read	104	0.5%
adw.adw_215	Read	104	0.5%
adw.adw_238	Read	104	0.5%
adw.adw_392	Read	104	0.5%
adw.adw_408	Read	104	0.5%
adw.adw_411	Read	104	0.5%
adw.adw_414	Read	104	0.5%
adw.adw_037	Read	103	0.5%
adw.adw_050	Read	103	0.5%
adw.adw_052	Read	103	0.5%
adw.adw_073	Read	103	0.5%
adw.adw_311	Read	103	0.5%
adw.adw_384	Read	103	0.5%
adw.adw_391	Read	103	0.5%
adw.adw_394	Read	103	0.5%
adw.adw_399	Read	103	0.5%
adw.adw_406	Read	103	0.5%
adw.adw_413	Read	103	0.5%
adw.adw_426	Read	103	0.5%
adw.adw_434	Read	103	0.5%
adw.adw_040	Read	102	0.5%
adw.adw_054	Read	102	0.5%
adw.adw_208	Read	102	0.5%
adw.adw_223	Read	102	0.5%
adw.adw_250	Read	102	0.5%
adw.adw_312	Read	102	0.5%
adw.adw_354	Read	102	0.5%
adw.adw_385	Read	102	0.5%
adw.adw_409	Read	102	0.5%
adw.adw_416	Read	102	0.5%
adw.adw_427	Read	102	0.5%
adw.adw_431	Read	102	0.5%
adw.adw_115	Read	101	0.5%
adw.adw_213	Read	101	0.5%
adw.adw_217	Read	101	0.5%
adw.adw_253	Read	101	0.5%
adw.adw_254	Read	101	0.5%
adw.adw_262	Read	101	0.5%
adw.adw_335	Read	101	0.5%
adw.adw_339	Read	101	0.5%
adw.adw_355	Read	101	0.5%
adw.adw_387	Read	101	0.5%
adw.adw_398	Read	101	0.5%
adw.adw_400	Read	101	0.5%
adw.adw_415	Read	101	0.5%
adw.adw_430	Read	101	0.5%
adw.adw_437	Read	101	0.5%
adw.adw_042	Read	100	0.5%
adw.adw_210	Read	100	0.5%
adw.adw_234	Read	100	0.5%
adw.adw_251	Read	100	0.5%
adw.adw_310	Read	100	0.5%
adw.adw_347	Read	100	0.5%
adw.adw_377	Read	100	0.5%
adw.adw_403	Read	100	0.5%
adw.adw_410	Read	100	0.5%
adw.adw_432	Read	100	0.5%
adw.adw_034	Read	99	0.5%
adw.adw_053	Read	99	0.5%
adw.adw_055	Read	99	0.5%
adw.adw_067	Read	99	0.5%
adw.adw_206	Read	99	0.5%
adw.adw_207	Read	99	0.5%
adw.adw_357	Read	99	0.5%
adw.adw_405	Read	99	0.5%
adw.adw_439	Read	99	0.5%
adw.adw_001	Read	98	0.5%
adw.adw_003	Read	98	0.5%
adw.adw_046	Read	98	0.5%
adw.adw_056	Read	98	0.5%
adw.adw_071	Read	98	0.5%
adw.adw_076	Read	98	0.5%
adw.adw_233	Read	98	0.5%
adw.adw_252	Read	98	0.5%
adw.adw_313	Read	98	0.5%
adw.adw_314	Read	98	0.5%
adw.adw_349	Read	98	0.5%
adw.adw_402	Read	98	0.5%
adw.adw_435	Read	98	0.5%
adw.adw_002	Read	97	0.5%
adw.adw_004	Read	97	0.5%
adw.adw_072	Read	97	0.5%
adw.adw_111	Read	97	0.5%
adw.adw_124	Read	97	0.5%
adw.adw_125	Read	97	0.5%
adw.adw_129	Read	97	0.5%
adw.adw_133	Read	97	0.5%
adw.adw_209	Read	97	0.5%
adw.adw_212	Read	97	0.5%
adw.adw_219	Read	97	0.5%
adw.adw_232	Read	97	0.5%
adw.adw_259	Read	97	0.5%
adw.adw_261	Read	97	0.5%
adw.adw_356	Read	97	0.5%
adw.adw_438	Read	97	0.5%
adw.adw_114	Read	96	0.5%
adw.adw_117	Read	96	0.5%
adw.adw_122	Read	96	0.5%
adw.adw_123	Read	96	0.5%
adw.adw_132	Read	96	0.5%
adw.adw_220	Read	96	0.5%
adw.adw_255	Read	96	0.5%
adw.adw_258	Read	96	0.5%
adw.adw_404	Read	96	0.5%
adw.adw_433	Read	96	0.5%
adw.adw_436	Read	96	0.5%
adw.adw_005	Read	95	0.5%
adw.adw_008	Read	95	0.5%
adw.adw_010	Read	95	0.5%
adw.adw_110	Read	95	0.5%
adw.adw_112	Read	95	0.5%
adw.adw_113	Read	95	0.5%
adw.adw_116	Read	95	0.5%
adw.adw_131	Read	95	0.5%
adw.adw_260	Read	95	0.5%
adw.adw_264	Read	95	0.5%
adw.health_risk	Read	95	0.5%
adw.adw_006	Read	94	0.5%
adw.adw_007	Read	94	0.5%
adw.adw_009	Read	94	0.5%
adw.adw_041	Read	94	0.5%
adw.adw_062	Read	94	0.5%
adw.adw_075	Read	94	0.5%
adw.adw_120	Read	94	0.5%
adw.adw_121	Read	94	0.5%
adw.adw_127	Read	94	0.5%
adw.adw_130	Read	94	0.5%
adw.adw_256	Read	94	0.5%
adw.adw_128	Read	93	0.5%
adw.adw_257	Read	93	0.5%
adw.adw_263	Read	93	0.5%
adw.adw_126	Read	92	0.4%
adw.sample	Read	87	0.4%
adw.catalog	Read	60	0.3%

// CUT THE COST

Your agent uses a handful of these tools. It pays for all 196.

You don't need all 196 of those definitions in the window. PolicyLayer is an MCP gateway that sits in front of AlpineDataWorks Intelligence Server: only the tools you grant are exposed to the agent, the rest never load. A smaller window means a sharper agent — less noise when it picks a tool — and every request costs less:

Grant scope	Definition cost	Reduction
All 196 tools (no gateway)	20,510 tokens	—
3 granted tools	~314 tokens	−98%
5 granted tools	~523 tokens	−97%
10 granted tools	~1,046 tokens	−95%

Create a free account and register AlpineDataWorks Intelligence Server — nothing to install.
Grant only the tools you use — ungranted definitions never enter the context window.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

CUT ALPINEDATAWORKS INTELLIGENCE TOKEN COST →

Instant setup, no code required.

Model your own stack in the token-cost calculator, or see the AlpineDataWorks Intelligence Server policy for what a sensible grant looks like.

//FAQ

AlpineDataWorks Intelligence Server token-cost questions.

How many tokens does the AlpineDataWorks Intelligence Server MCP server use?+

Its 196 tool definitions total 20,510 tokens — 10% 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 AlpineDataWorks Intelligence 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 AlpineDataWorks Intelligence Server's token usage?+

Expose fewer tools. A PolicyLayer grant scopes AlpineDataWorks Intelligence 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 314 tokens, a 98% 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.

// METHOD & REVIEW

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 23-07-2026 from the PolicyLayer scan database over all 196 catalogued AlpineDataWorks Intelligence Server tools. Counts refresh with every site build.