Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Signs, hash-chains, and verifies compute usage receipts via an MCP server, no issuer trust required.
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。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"computeledger": {
"command": "uvx",
"args": [
"computeledger-cli",
"computeledger",
"mcp"
]
}
}
}ターミナルでこれを実行します
claude mcp add computeledger -- uvx computeledger-cli computeledger 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": {
"computeledger": {
"command": "uvx",
"args": [
"computeledger-cli",
"computeledger",
"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": {
"computeledger": {
"type": "stdio",
"command": "uvx",
"args": [
"computeledger-cli",
"computeledger",
"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。
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。
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。
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 RudrenduPaul 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
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
パソコンで動き、代わりにローカルの設定を使います
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
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。
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。
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)。
GitHubリポジトリの最終更新は–、最新バージョン(0.1.3)がレジストリに届いたのは3 か月前です。
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Python (uv)も必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, AvaHR
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Read hiring data and create inactive assessment and questionnaire drafts for human review.
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Install • Quickstart • Features • CLI reference • Comparison • FAQ
Sign, hash-chain, and independently verify compute usage, portable across any provider.
ComputeLedger records a compute job's usage (GPU-hours, hardware, duration, workload type) as a cryptographically signed receipt and appends it to a tamper-evident local ledger. Anyone can verify a receipt's authenticity and the ledger's integrity without trusting the issuer, and without buying into any single cloud, chain, or vendor's stack.
npm install -g computeledger-cli
pip install computeledger-cli
Both packages install the same computeledger command. Receipts are interoperable either way: a receipt signed by the npm binary verifies correctly with the PyPI binary, and vice versa.
$ computeledger keys generate --local
Generated Ed25519 keypair. Public key: COxK/lkoWxWB42QKXjvcHnmBPozH4Oo2JHoOKDjsoU8= Private key: ./.computeledger/keys/ed25519.pem (mode 600)
$ computeledger record --local --provider aws --hardware nvidia-h100 \ --duration-seconds 3600 --gpu-hours 1 --workload-type training Recorded usage receipt 39952199-0897-48b8-92c5-e351f773c83d.
$ computeledger ledger verify --local Ledger valid: 1 entries, unbroken hash chain.
Or wrap a real job directly, no manual record call needed:
computeledger run --local --provider on-prem --hardware nvidia-a100 -- python train.py
run executes the wrapped command as a real subprocess (never through a shell), measures wall-clock duration, samples GPU utilization via nvidia-smi when one is present, and signs + appends the resulting receipt automatically. On a machine with no NVIDIA GPU, it still produces a duration-only receipt.
Give the receipt to anyone, on any machine, with no ComputeLedger account and no network call:
computeledger verify receipt.json
Multi-cloud and multi-provider GPU usage has no portable, verifiable record. A cost dashboard tells you what a provider says you used; it does not let a third party independently confirm that record wasn't altered after the fact, and it only works with the providers it integrates with. ComputeLedger is a lightweight, provider-agnostic attestation format: any process that can run a CLI command or call an MCP tool can produce a receipt, and any process, in any language, can verify one.
This is deliberately narrow. It does not compete with GPU marketplaces, cost dashboards, or confidential-computing platforms, all of which do real, different jobs. See the comparison below for exactly where the line is.
ledger verify detects, even if the tampered entry's own signature still looks locally valid.computeledger binary verifies correctly against the PyPI package's computeledger binary. Both implementations serialize the receipt payload through the same deterministic canonical-JSON algorithm before hashing.--json for structured output, and computeledger mcp starts a Model Context Protocol server exposing record_usage, verify_receipt, list_ledger, and verify_ledger as callable tools.computeledger run -- executes the wrapped command via an argument array, never a shell string, so metacharacters in the wrapped command are inert.computeledger keys generate [--local]
computeledger keys show [--local] [--json] computeledger run [--local] [--provider ] [--hardware ] [--workload-type training|inference|unknown] [--no-record-command] [--json] -- computeledger record --provider --hardware --duration-seconds [--gpu-hours ] [--flops ] [--workload-type ] [--local] [--json] computeledger verify [--json] computeledger ledger list [--local] [--json] computeledger ledger show [--local] [--json] computeledger ledger verify [--local] [--json] computeledger export --format json|csv [--out ] [--local] computeledger mcp
| Flag | Meaning |
|---|---|
--local | Use ./.computeledger in the current directory instead of ~/.computeledger |
--json | Structured JSON on stdout instead of human-readable text |
--no-record-command | Omit the wrapped command string from the receipt (run only) |
ComputeLedger ships a Model Context Protocol server so an AI agent (Claude, Cursor, or any MCP-compatible client) can record and verify compute usage receipts directly, without a human invoking the CLI by hand. The Python package exposes the server as a subcommand rather than a separate console script, so computeledger mcp is the real invocation, not computeledger-mcp.
pip install computeledger-cli
Add it to your MCP client's config (for Claude Desktop, claude_desktop_config.json):
{
"mcpServers": { "computeledger": { "command": "uvx", "args": ["--from", "computeledger-cli", "computeledger", "mcp"] } } }
The server exposes four tools, matching the CLI one-for-one:
record_usage(provider, hardware, durationSeconds, gpuHours?, estimatedFlops?, workloadType?, local?): signs a usage entry with the local Ed25519 key, appends it to the hash-chained ledger, and returns the signed receipt.verify_receipt(receipt): independently verifies a receipt's signature and hash integrity.list_ledger(local?): lists every receipt recorded in the local ledger.verify_ledger(local?): verifies every entry's signature plus the unbroken hash chain across the whole ledger.Example call:
record_usage(provider="lambda-labs", hardware="nvidia-h100", durationSeconds=3600, gpuHours=1, workloadType="training")
Transport is stdio, so there is nothing to host: the MCP client spawns the server as a local subprocess. Source: python/src/computeledger/mcp/server.py.
The npm package exposes the same four tools through the native TypeScript server (src/mcp/server.ts), invoked the same way with npx computeledger-cli mcp. Both implementations are cross-verified interoperable, and every tool returns the same structured JSON shape the CLI's --json mode produces.
import { createReceipt, verifyReceipt, Ledger, verifyChain, loadKeyPair, resolvePaths } from "computeledger-cli";
from computeledger import create_receipt, verify_receipt, Ledger, verify_chain, load_key_pair
ComputeLedger occupies a narrow, specific gap: a portable, cryptographically verifiable usage receipt that doesn't require adopting any single provider's chain or platform. It is not trying to replace the tools below, each of which does a real, different job.
| ComputeLedger | SkyPilot | OpenCost | AICert | |
|---|---|---|---|---|
| What it is | Signed, portable usage receipts | Multi-cloud job orchestration + cost | Kubernetes/cloud cost monitoring | Training-provenance attestation |
| Cryptographic verification | Yes (Ed25519, offline) | No | No | Yes (TPM-based) |
| Provider lock-in | None | Orchestrates specific clouds | Kubernetes/cloud-native | None |
| Tamper-evident history | Yes (hash-chained ledger) | No | No | No (single artifact, no chain) |
| GitHub stars | New | 10,441 | 6,659 (CNCF) | 20 |
| Project activity | Active | Active | Active | No commits since June 2024 |
Agent-native (MCP/--json) | Yes | Partial (API/SDK) | No | No |
SkyPilot and OpenCost solve real, adjacent problems (running jobs across clouds, and visualizing what they cost) at far larger scale and maturity than this project. Neither produces a signed, independently verifiable usage record. AICert attempted training-compute provenance as a standalone OSS tool using TPM-bound attestation and has had no commits since June 2024; ComputeLedger's scope is deliberately narrower (a usage receipt, not a full training-provenance framework) and ships both an npm and a PyPI package from day one specifically so the receipt format isn't locked to one language's ecosystem.
ComputeLedger is an open-source CLI, library, and MCP server for producing and verifying cryptographically signed records of compute usage. It exists because compute usage claims (GPU-hours consumed, hardware used, workload duration) currently have no portable, offline-verifiable proof format: a billing dashboard is only as trustworthy as the provider issuing it, and it only covers that one provider. ComputeLedger's receipts are self-contained, signed JSON objects that any party, on any machine, in either of two independently maintained language implementations, can verify without a network call or a trusted third party.
Does ComputeLedger require an account or API key? No. Everything runs locally. Keys are generated and stored on your own machine (~/.computeledger or ./.computeledger with --local).
Can a receipt be forged? Not without the private key used to sign it. verify recomputes the payload hash and checks the Ed25519 signature against the embedded public key; the public key itself is part of the signed payload, so substituting a different key changes the hash and invalidates the receipt.
What happens if there's no GPU? computeledger run degrades gracefully: it records wall-clock duration and whatever --hardware/--provider you specify, and simply omit
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