Home / Token cost / Goodmem

The Goodmem MCP server costs 12,319 tokens before the first call.

Connect Goodmem and its 44 tool definitions are loaded into the model's context on every request — 6.2% of a 200k window spent before your agent does anything.

QUICK ANSWER The Goodmem MCP server's tool definitions consume 12,319 tokens — 6.5× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 44 tools · 12,319 tokens · 6.2% of 200k · 1.2% 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 6.2%
1M WINDOW 1.2%

Corpus context: Goodmem ranks #128 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 12,319 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 280 tokens per tool.

ToolCategoryTokens% of server
goodmem_llms_create Write 1,196 9.7%
goodmem_llms_update Write 1,085 8.8%
goodmem_embedders_create Write 845 6.9%
goodmem_memories_retrieve Read 822 6.7%
goodmem_rerankers_create Write 763 6.2%
goodmem_spaces_create Write 726 5.9%
goodmem_embedders_update Write 680 5.5%
goodmem_memories_batch_create Write 667 5.4%
goodmem_memories_create Write 629 5.1%
goodmem_rerankers_update Write 627 5.1%
goodmem_spaces_list Read 366 3.0%
goodmem_memories_list Read 358 2.9%
goodmem_ocr_document Execute 217 1.8%
goodmem_rerankers_list Read 213 1.7%
goodmem_llms_list Read 205 1.7%
goodmem_embedders_list Read 199 1.6%
goodmem_memories_batch_delete Destructive 188 1.5%
goodmem_memories_pages Read 188 1.5%
goodmem_spaces_update Write 164 1.3%
goodmem_apikeys_update Write 156 1.3%
goodmem_lookup_model Read 153 1.2%
goodmem_apikeys_create Write 151 1.2%
goodmem_admin_background_jobs_purge Destructive 149 1.2%
goodmem_configure Write 130 1.1%
goodmem_admin_drain Read 124 1.0%
goodmem_memories_get Read 120 1.0%
goodmem_memories_batch_get Read 119 1.0%
goodmem_users_get Read 112 0.9%
goodmem_rerankers_get Read 73 0.6%
goodmem_embedders_get Read 72 0.6%
goodmem_rerankers_delete Destructive 71 0.6%
goodmem_llms_get Read 71 0.6%
goodmem_embedders_delete Destructive 70 0.6%
goodmem_apikeys_delete Destructive 69 0.6%
goodmem_llms_delete Destructive 69 0.6%
goodmem_spaces_get Read 68 0.6%
goodmem_memories_delete Destructive 66 0.5%
goodmem_spaces_delete Destructive 66 0.5%
goodmem_client_info Read 59 0.5%
goodmem_admin_license_reload Read 45 0.4%
goodmem_apikeys_list Read 43 0.3%
goodmem_system_info Execute 42 0.3%
goodmem_users_me Read 42 0.3%
goodmem_system_init Read 41 0.3%

Most agents use a handful of these tools. They pay for all 44.

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 (280 tokens each).

Grant scopeDefinition costReduction
All 44 tools (no gateway) 12,319 tokens
3 granted tools ~840 tokens −93%
5 granted tools ~1,400 tokens −89%
10 granted tools ~2,800 tokens −77%

Goodmem token-cost questions.

How many tokens does the Goodmem MCP server use?+

Its 44 tool definitions total 12,319 tokens — 6.2% 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 Goodmem 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 Goodmem's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Goodmem 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 840 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 44 catalogued Goodmem tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Goodmem to the tools you actually allow. Ungranted definitions never load, and every call that does run is checked against policy first.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.