Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
LLM pricing where every rate carries its source and the date it was last confirmed.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
ウェブ、デスクトップ、モバイルのClaudeで使えます:
https://promptspend.dev/mcpClaude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}ターミナルでこれを実行します
claude mcp add --transport http promptspend https://promptspend.dev/mcpclaude mcp add promptspend -- npx -y @promptspend/mcpプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"promptspend": {
"url": "https://promptspend.dev/mcp"
}
}
}プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"promptspend": {
"type": "http",
"url": "https://promptspend.dev/mcp"
}
}
}プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"promptspend": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
ChatGPTがつなげるのは、インターネット上のサーバーだけです:
https://promptspend.dev/mcpメニューの名前は、プランや時期によって少し違うことがあります。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
ツールの名前と説明は、サーバーが2026年11月7日に返したそのままの内容です。書いたのは作者で、私たちではありません。プレーンテキストとして表示しています。
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.
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント AndrewAvery7 で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
69ミリ秒で応答(HTTP 200)
サインイン不要
3件のツールの説明を読み、AIへの隠れた指示は見つかりませんでした
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック3項目のうち3項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
どちらでも使えます。パソコンにインストールしても、クラウド版につないでもかまいません。
GitHubリポジトリの最終更新は–、最新バージョン(0.1.11)がレジストリに届いたのは8 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。URLでつながるので、リモートMCPサーバーに対応したアプリ(開発者モードのChatGPTなど)でも使えます。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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.