Home / Token cost / Himalayas Remote Jobs

The Himalayas Remote Jobs MCP server costs 6,819 tokens before the first call.

Connect Himalayas Remote Jobs and its 41 tool definitions are loaded into the model's context on every request — 3.4% of a 200k window spent before your agent does anything.

QUICK ANSWER The Himalayas Remote Jobs MCP server's tool definitions consume 6,819 tokens — 3.6× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 41 tools · 6,819 tokens · 3.4% of 200k · 0.7% 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 3.4%
1M WINDOW 0.7%

Corpus context: Himalayas Remote Jobs ranks #909 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 6,819 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 166 tokens per tool.

ToolCategoryTokens% of server
search_jobs Read 592 8.7%
create_company_job Write 561 8.2%
update_company_job Write 512 7.5%
post_job_public Write 386 5.7%
add_company_perk Write 259 3.8%
search_companies Read 248 3.6%
save_job Write 244 3.6%
add_experience Write 242 3.5%
update_company_profile Write 237 3.5%
update_profile Write 211 3.1%
add_education Write 197 2.9%
get_salary_data Read 185 2.7%
get_remote_work_statistics Read 183 2.7%
search_talent Read 147 2.2%
update_job_status Write 147 2.2%
purchase_job_extras Read 142 2.1%
send_message Write 142 2.1%
start_conversation Execute 134 2.0%
get_companies Read 127 1.9%
update_company_tech_stack Write 127 1.9%
get_jobs Read 126 1.8%
get_job_details Read 119 1.7%
mark_message_read Write 118 1.7%
get_talent_profile Read 108 1.6%
get_related_jobs Read 107 1.6%
get_conversation Read 102 1.5%
update_tech_stack Write 100 1.5%
delete_conversation Destructive 98 1.4%
check_job_payment_status Read 89 1.3%
get_company_details Read 89 1.3%
remove_company_perk Destructive 86 1.3%
get_correct_country_name Read 86 1.3%
list_company_jobs Read 81 1.2%
show_company_job Read 81 1.2%
delete_company_job Destructive 80 1.2%
remove_saved_job Destructive 70 1.0%
list_conversations Read 63 0.9%
get_company_perks Read 52 0.8%
get_company_profile Read 48 0.7%
get_saved_jobs Read 47 0.7%
get_my_profile Read 46 0.7%

Most agents use a handful of these tools. They pay for all 41.

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 (166 tokens each).

Grant scopeDefinition costReduction
All 41 tools (no gateway) 6,819 tokens
3 granted tools ~499 tokens −93%
5 granted tools ~832 tokens −88%
10 granted tools ~1,663 tokens −76%

Himalayas Remote Jobs token-cost questions.

How many tokens does the Himalayas Remote Jobs MCP server use?+

Its 41 tool definitions total 6,819 tokens — 3.4% 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 Himalayas Remote Jobs 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 Himalayas Remote Jobs's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Himalayas Remote Jobs 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 499 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 41 catalogued Himalayas Remote Jobs tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Himalayas Remote Jobs 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.