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PopNx MCPAIを使う人のためのMCPサーバー · やさしい言葉で解説

Compute Finance Oracle

作者:compute-financeバージョン 0.12.2i★ –iパソコンで動く
レジストリ名io.github.compute-finance/mcp

インストール

概要

作者による説明(英語の原文)

Live AI compute pricing oracle — real-time LLM model prices across providers

PopNx MCPの3つの柱

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。

できること

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。

  • 接続方法: パソコンで動く
  • トランスポート: stdio
  • カテゴリ: 会計・財務・税務

インストール

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。

インストールの前に

  • 基本チェック:1項目中1項目に合格
  • あなたのパソコンで、あなたのユーザーと同じ権限(ファイル、ネットワーク、アプリ)で動きます。
  • レジストリには、入力が必要なキーは載っていません。

必要なもの

  • npmNode.jsのインストール(npxコマンドに必要)

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。

Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます

{
  "mcpServers": {
    "compute-finance": {
      "command": "npx",
      "args": [
        "-y",
        "@compute-finance/mcp",
        "setup"
      ]
    }
  }
}

ターミナルでこれを実行します

claude mcp add compute-finance -- npx -y @compute-finance/mcp setup

Cursorで開く

または、設定を自分でコピーする

プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます

{
  "mcpServers": {
    "compute-finance": {
      "command": "npx",
      "args": [
        "-y",
        "@compute-finance/mcp",
        "setup"
      ]
    }
  }
}

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。

VS Codeで開く

または、設定を自分でコピーする

プロジェクトの .vscode/mcp.json に貼り付けます

{
  "servers": {
    "compute-finance": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@compute-finance/mcp",
        "setup"
      ]
    }
  }
}

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。

これはパソコンで動くサーバーですが、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。ほかのアプリを選んでください。

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。

できること

パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。

基本チェックi

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

  • ✓ 合格

    作者とリポジトリが一致

    レジストリ名は、リポジトリを所有するGitHubアカウント compute-finance で確認済みです

  • … 未チェック

    今もメンテナンスされている

    このリポジトリはまだGitHubから読み取っていません

  • … 未チェック

    ライセンスがある

    このリポジトリはまだGitHubから読み取っていません

  • – 対象外

    エンドポイントが応答する

    これはパソコンで動くため、インターネット上のエンドポイントはありません

  • – 対象外

    OAuthでサインイン

    パソコンで動き、代わりにローカルの設定を使います

  • … 未チェック

    ツールの説明に隠れた指示がない

    パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです

会計・財務・税務のほかのサーバーとの比較i

  • このカテゴリのほかの1734件のうち0%よりスターが多い
  • このカテゴリのほかの1734件のうち80%より最近レジストリに公開
  • 基本チェックの結果が、このカテゴリのほかの1734件のうち100%と同じかそれ以上

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。

よくある質問

Compute Finance Oracleは安全ですか?

当てはまる基本チェック1項目のうち1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。

どんなキーやコードが必要ですか?

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。

パソコンで動きますか、それともクラウドですか?

あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。

今もメンテナンスされていますか?

GitHubリポジトリの最終更新は–、最新バージョン(0.12.2)がレジストリに届いたのは9 日前です。

どのアプリで使えますか?

Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。

代わりに使えるものは?

同じカテゴリには:Finance Toolkit, SolidInvoice, Korea Tax Calculator

代わりの候補

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

Finance Toolkit

作者:JerBouma

500+ transparent financial methods calculated from raw statements, not third-party endpoints.

パソコンでもクラウドでも★ 5,496MIT今日更新
インストール

SolidInvoice

作者:SolidInvoice

Create and send invoices and quotes, manage clients, and track payments in SolidInvoice.

クラウドで動く★ 981MIT8 日前更新
インストール

作者のREADME

READMEを開く

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。

@compute-finance/mcp

Live AI compute pricing oracle — real-time LLM model prices across nine vendors (Anthropic, OpenAI, Google, DeepSeek, xAI and four more) via the Compute Finance Oracle.

A stdio MCP server. Works in any MCP client. Includes optional Claude Code skills for session cost analysis.

Quick start

Claude Code (recommended)
npx @compute-finance/mcp setup

This single command:

  1. Registers the MCP server at user scope (claude mcp add)
  2. Installs Claude Code skills (/cf-session-management, /cf-session-consumption, /cf-active-sessions, /cf-account)
  3. Installs the cost hook — a UserPromptSubmit hook that injects session cost into Claude's context so every response can show how much you've spent

Restart Claude Code after setup.

To also answer for your own Compute Finance account, connect it once — see Your account.

Or register manually without skills/hook:

claude mcp add --scope user compute-finance -- npx @compute-finance/mcp
Cursor / VS Code / Any MCP client

Add to your MCP config (.cursor/mcp.json, VS Code settings, etc.):

{

"mcpServers": { "compute-finance": { "command": "npx", "args": ["@compute-finance/mcp"] } } }

From source
git clone https://github.com/compute-finance/mcp.git

cd mcp npm install && npm run build npx . setup

Tools

Tools are named by layer prefix and come in three groups. The public and local groups need no API key and no account.

GroupPrefixesWhat the tools touch
Public datadata_get_*, compute_*Read the live Compute Finance Oracle over unauthenticated GETs
Local sessionsrender_*, analyze_*, telemetry_get_*Read Claude Code transcripts on this machine, price them at live oracle rates, and append the measurements to the session logs under ~/.compute-finance/, which telemetry_get_history reads back; nothing is uploaded
Your accountaccount_*Registered only once an app grant is connected, and only the tools that grant allows. With Read access they read the account's balance, spend, keys and history; with Act access some of them act on the account — create an API key, freeze or unfreeze a key, set budget caps, configure auto top-up

The account_* tools are not listed below: the exchange publishes them, and this package copies none of them. See Your account.

Data (live oracle)
ToolDescription
data_get_basketEvery model in the current SCU index, with provider, family (e.g. openai.gpt, anthropic.claude), base_* and billed_* USD prices per million tokens, per-component cache pricing (read, write-5m, write-1h), a reasoning output price and the long-context price ladder
data_get_pricePrice for a single model (e.g. anthropic/claude-opus-4.8) — index members and catalog-only entries on identical terms, with the same per-component cache and reasoning prices and the long-context price ladder
data_get_scuCurrent Standard Compute Unit — value plus a methodology-versioned breakdown listing every family representative
data_get_breakdownPer-family blended-cost breakdown alone — methodology-versioned discriminated union with one entry per family representative
data_get_cpiFull Compute Price Index as last attested on-chain — scuUsd, revisionVersion, the raw and marked-up prices that revision published
data_get_reconstitutionsHistorical index changes — model swaps, SCU before/after
data_get_methodologyMethodology changelog — every version with its formula summary and spec link, plus the version in force
data_get_historySCU index time series over a date range — per-revision, daily, or weekly granularity; daily/weekly buckets carry the last revision's value forward across empty buckets
data_get_model_price_historyPer-model input/output USD price time series for any oracle-tracked model — same granularity semantics as data_get_history, with catchup gaps surfaced in unavailableRevisions
data_get_catalogEvery model with a recorded price, index members and non-index entries alike — indexMember flag, current price with its provenance pair, cache and reasoning components, and the raw upstream contextTiers / maxInputTokens
data_get_model_price_atPer-model input/output USD price effective at a timestamp — manifest source when the model represented its family in the revision active then, catalog otherwise
data_get_baselineFrozen SCU denominator behind computeIndex — the SCU of the first confirmed revision, set once and never recomputed
data_get_scu_atSCU value active at a timestamp via step function — no interpolation, null before the genesis revision
data_get_model_availabilityWhich models can serve right now — every catalogue model with a routable flag, the model auto points at, and the computedAt / ttlSeconds the answer is good for

Models are identified by their canonical vendor-prefixed id — anthropic/claude-opus-4.8, openai/gpt-5.5, qwen/qwen-3.5-flash. Every tool taking a model also accepts the bare name (gpt-5.5) and answers with the canonical id. The vendor slug is not always the provider key (alibaba → qwen, xai → x-ai, moonshot → moonshotai), so reuse an id the API returned rather than assembling one. data_get_scu, data_get_breakdown and data_get_reconstitutions are the exception: they pass the attested manifest through verbatim and so report bare model keys, because a / is not a legal manifest key.

Cache pricing comes from the Compute Finance Oracle. Session and consumption reports show effective (cache-aware) cost when the oracle has published the relevant cache components; otherwise they show nominal cost (input rate applied to every input variant) and label effective as unavailable for that model.

Alongside cache, the oracle publishes a reasoning output price — reasoning.reasoningOutput, on the same base as every other component; the whole reasoning block is null for a model with no usable reasoning price. It is catalogue data. Session and consumption reports do not bill it: Claude Code transcripts count thinking blocks rather than reasoning tokens, and those tokens are already inside output_tokens.

Every price is reported on two bases: base_* is the provider list price, identical for every model the oracle tracks, and billed_* is what compute.finance charges — base × (1 + routing_fee_rate). Compare models on base_*, budget on billed_*. The rate ships once per response and billed_* is null when the oracle does not publish it. Session and consumption reports are on the base basis throughout.

Every current-price answer comes from one place: the live catalogue the exchange bills against. Index membership decides which models data_get_basket and compute_compare list, never what a model costs, so two models the catalogue prices alike quote alike. data_get_cpi is the exception by design — it serves the prices the latest on-chain revision attested, which change only when an operator publishes the next one and may therefore lag the catalogue. Read it as attestation history, not as a quote.

Some models get pricier past a context length. data_get_basket and data_get_price publish that as context_tiers, a ladder ascending by from_input_tokens and always at least one rung: the first starts at 0 and restates the flat rate, so a model priced the same at every size has exactly one rung and nothing has to branch on whether a model happens to be tiered. Rungs carry base_* and billed_* like every other price; only the flat rate enters the SCU index. compute_estimate and compute_compare pick the rung from the whole input side of the request — prompt plus cache reads plus cache writes, all charged at the full input rate there since neither tool applies a cache discount — over half-open ranges, so an input landing exactly on a threshold takes that rung, and both return the chosen rung as applied_context_tier so the rate behind the number is visible. data_get_catalog passes the oracle document through unchanged, so there contextTiers is absent rather than one-rung on a flat model.

The ladder comes from the catalog endpoint, and the two kinds of tool part ways whenever it cannot answer for a model — the read failed, or it succeeded and the model was not in it, which is upstream drift rather than a flat price. data_get_basket and data_get_price still serve their prices and set context_tiers to null — an unknown ladder, never a one-rung stand-in for a ladder nobody read. compute_estimate and compute_compare error instead: a cost quoted at the flat rate would understate exactly the long context the ladder exists to price.

max_input_tokens is the largest input a model accepts, null when the model declares no window of its own — not unbounded: the request-body ceiling still applies, there is just no per-model limit. Above a declared window the oracle refuses the request outright, so compute_estimate and compute_compare set exceeds_max_input_tokens. They still quote the cost: these tools are read-only and an agent sizing a context needs the number before it reshapes the request, but the flag says plainly that the request as supplied would be rejected.

Prices also carry a provenance mark saying how far the number has been checked: verified — an operator recorded a vendor source for it; inferred — derived from a sibling number or a vendor default, with no source recorded; promotional — a discounted list price that is expected to end. Every value bills as shown; the mark says how much to trust it, not what it costs. Marks are set by hand and hold as of the operator's last pass, not as a live check against the vendor. Every cache and reasoning component carries its own mark wherever it appears, and so does every base price: data_get_catalog marks currentPrice.provenance for every model, index member or not, while data_get_basket, data_get_price and compute_estimate carry the same pair as base_price_provenance. A rung follows the same rule: the first repeats the base price's mark, and a higher rung is always a catalogue number, marked in both directions with the single mark the vendor quotes it under. Session and consumption reports print each cache multiplier with its mark; when the oracle publishes no cache pricing for a model they say so and print no marks.

A price says what a model costs, never whether it can serve. data_get_model_availability answers that second question: the public pool's routability list — every catalogue model under its canonical id with a routable flag, plus the model auto points at. Call it before settling on a model, so one that cannot serve is ruled out before the request instead of by a 503 after it. The signal is advisory: true as of computedAt and only for ttlSeconds after it, capacity moves on its own, and a model reported routable can still fail the send — the caller still handles the error on the request itself. The answer is the exchange's own, served verbatim with no liveness computed here, so it cannot disagree with the router that has to honour it; it is cached for exactly the ttlSeconds published with it, and not at all when that figure is missing or unusable, rather than for the window every other read use

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