Context7
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제작자가 쓴 설명(영어 원문)
LLM pricing where every rate carries its source and the date it was last confirmed.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://promptspend.dev/mcpClaude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add --transport http promptspend https://promptspend.dev/mcpclaude mcp add promptspend -- npx -y @promptspend/mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"promptspend": {
"url": "https://promptspend.dev/mcp"
}
}
}프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"promptspend": {
"type": "http",
"url": "https://promptspend.dev/mcp"
}
}
}프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"promptspend": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://promptspend.dev/mcp메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
도구 이름과 설명은 서버가 2026년 11월 7일에 알려 준 그대로예요. 우리가 아니라 제작자가 쓴 글이고, 일반 텍스트로만 보여 드려요.
get_priceList price for one LLM in USD per million tokens, with its source and last-confirmed date. Returns disputed=true when two sources disagree, and close matches when the model is unknown. To cost a workload, use estimate_cost.
estimate_costMonthly cost of a described workload, or of measured usage, on each model. Accounts for history compounding, cache writes, long-context tiers and reasoning tokens. Use this instead of multiplying prices by hand.
find_cheaperCheaper models that score at or above the given one on capability, costed on the same workload. Returns candidates to test, not a recommendation.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 AndrewAvery7 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
69ms 만에 응답(HTTP 200)
로그인 필요 없음
도구 설명 3개를 읽었고, AI에게 보내는 숨겨진 지시는 없었어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 3개 중 3개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
둘 다 가능해요. 내 컴퓨터에 설치해도 되고, 클라우드 버전에 연결해도 돼요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.11)은 8일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
LLM pricing for coding agents — every price carries its source and the date it was last confirmed.
There are already MCP servers that will tell you what a model costs. This one tells you where the number came from, when it was last checked, and which numbers two sources disagree about. And it will cost a described workload rather than just quoting a rate, because the arithmetic between "$3 per million tokens" and "$4,955 a month" is where nearly every estimate goes wrong.
claude mcp add promptspend -- npx -y @promptspend/mcp
No API key. No account. MIT.
Every price comes with its paperwork.
// Real output, not an illustration: `get_price` for gpt-5 on 2026-08-03.
{ "model": "gpt-5", "input_per_million_usd": 1.25, "output_per_million_usd": 10, "provenance": { "source": "vendor", "source_description": "Hand-verified against the provider's own published pricing page.", "last_verified": "2026-08-03", "disputed": false, "upstream_stale": false, "confidence": "Confirmed against the provider's own pricing page on 2026-08-03.", "verified_url": "https://developers.openai.com/api/docs/pricing", }, }
last_verified moves as rows are re-read, so the date here will age; the shape will not. What is worth checking is that the block is present at all — a price without one is a price you cannot audit.
That matters more to an agent than to a person. Someone reading a web page sees the interface around the number and forms their own view of how much to trust it. A model handed a bare figure has nothing, and will repeat it with whatever confidence the phrasing implies. Given the date and the source it can say _"as of 1 August, per OpenAI's pricing page"_ instead of stating a number as though it were timeless.
And it says when it does not know. Where two sources disagree and no human has adjudicated, the response is marked disputed: true with both figures, rather than quietly picking one:
DISPUTED — two sources disagree on this price and no human has adjudicated it.
The figure shown is the primary feed's number, last confirmed 2026-08-02. Treat it as indicative and say so.
estimate_cost runs promptspend.com's own cost engine — imported, not reimplemented, so a number this server reports and a number the website shows cannot drift apart. A test asserts it.
It accounts for the things hand-rolled estimates miss:
_"What would a support assistant cost at 4,000 conversations a day on GPT-5
versus DeepSeek V3.2?"_
MCP tool definitions are loaded into your context on every turn, for as long as the server is connected. A heavy server can add thousands of tokens to every message, which is why the advice going round is to prefer a CLI for read-only data.
A pricing server that quietly taxes every turn, to tell you about token costs, would be an easy and deserved joke. So:
This server adds ~897 tokens per turn. Three tools, short descriptions,
budgeted at 1,200 and checked in CI by
npm run check:footprint.
If that number ever stops being true, the build fails — the README figure is asserted against the actual manifest, not typed once and trusted.
| Tool | What it answers |
|---|---|
get_price | What does this model cost, and how much should I trust that? With as_of, what did it cost then? |
estimate_cost | What will _my_ workload cost per month on each? With usage, what would these totals have cost? |
find_cheaper | What could I test that is cheaper and not obviously worse? |
`get_price` with `as_of: "2026-09-15"` returns the rate PromptSpend published on that UTC day, read from the price history — useful for checking an old invoice. A day before PromptSpend began tracking the model is answered "not tracked", never with the first rate seen, and a later correction (PromptSpend had been reading the wrong page) is returned with the figure, so an agent does not present a misreading as what the vendor charged.
`estimate_cost` with `usage` prices measured totals — say a month from a usage export — instead of a described workload: { "input": 1200000, "output": 300000, "cache_read": 4000000, "cache_write": 200000 }, where input excludes cache reads. A model without a published cache rate is billed at its full input rate (and the response says so), and a model with a long-context tier is flagged, because totals cannot show which requests crossed it.
find_cheaper returns candidates to test, never a recommendation — the capability index behind it is an illustrative estimate, not a benchmark, and the response says so.
Claude Code
claude mcp add promptspend -- npx -y @promptspend/mcp
Cursor / Claude Desktop / Windsurf — in mcp.json:
{ "mcpServers": { "promptspend": { "command": "npx", "args": ["-y", "@promptspend/mcp"] } } }
No install — hosted at https://promptspend.dev/mcp. Same tools, same answers, imported from this package rather than copied. In Claude.ai or ChatGPT, add it as a custom connector with that URL; in Claude Code:
claude mcp add --transport http promptspend https://promptspend.dev/mcp
The hosted server receives your tool arguments (a model name, a workload's token counts) to answer them, and stores none of them; it needs no key or account. Like the rest of the pricing API at promptspend.dev, it has Cloudflare's request logging switched on: each entry records the time, the address (/mcp), request headers and connection details such as an IP address, kept for up to seven days. PromptSpend's code never logs the arguments themselves. Run the npm package instead if a workload description should never leave your machine.
Or as an Agent Skill, which costs context only when a price question comes up rather than on every turn: see skills/.
catalog_status warning written for the agent to relay. Past that bounded grace period, the tools return an error rather than an old number.Standard-tier, global-endpoint list prices in USD. Not modelled: regional and data-residency premiums, priority tiers, server-side tool-call fees, fine-tuning, and negotiated or committed-use discounts.
The same catalog is also in free native apps for iPhone and iPad and Android.
Part of PromptSpend — MIT. Found a wrong price? That is the most serious class of bug this project can have. Open an issue.