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)
Code & logic calculator for AI agents: 31 languages, symbolic math, SMT/logic solving, Big-O.
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": {
"codecalc": {
"command": "uvx",
"args": [
"codecalc"
]
}
}
}ターミナルでこれを実行します
claude mcp add codecalc -- uvx codecalcプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"codecalc": {
"command": "uvx",
"args": [
"codecalc"
]
}
}
}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": {
"codecalc": {
"type": "stdio",
"command": "uvx",
"args": [
"codecalc"
]
}
}
}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 The-40-Thieves 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(0.13.0)がレジストリに届いたのは2 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, sync. labs
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.
Lipsync videos, dub them into other languages, make talking videos and open sync. Studio.
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。
**codecalc is an offline, self-hosted MCP server that gives an AI agent a calculator, a code runner, and a logic checker — so it gets a *correct* answer instead of a guessed one. It runs code in 31 languages, does exact symbolic math, solves SMT/logic problems, and measures complexity, all exposed as 51 MCP tools**.
Fastest path: uvx 'codecalc[full]' setup --write registers codecalc with your MCP client automatically. New to MCP, or want more detail first? See QUICKSTART.md, or the Install section below.
Three things nobody else offers together cleanly:
install_package, the runtime-update tools, executed code unless no_net, and a one-time in-process grammar download on first analyze_complexity — full breakdown in the network-boundary table below).verify_translation proves a port to another language behaves identically, verify_optimization proves an optimization preserved behavior, and z3_check proves or refutes logic with an SMT solver.Use it when you want a free, local, private, hardened code-runner and verifier that an MCP agent can call directly — no vendor account, no cloud spend, nothing leaving the machine except where a tool's job explicitly requires it.
Reach for something else when you want managed cloud scale instead of self-hosting (a hosted sandbox like E2B or Modal), or when you're not self-hosting at all and the model vendor's built-in code interpreter already covers what you need.
codecalc is not a general cloud sandbox and not a vendor code interpreter. It overlaps with several things and beats them in only one narrow place — forcing a model to *measure* a claim instead of asserting it. Where that isn't what you need, one of these is the better tool, and this table says so plainly.
| You want… | Better fit | Why |
|---|---|---|
| To just run some Python/JS quickly, zero setup | Your model vendor's built-in interpreter | Already there, already sandboxed, nothing to install. Anthropic's code-execution tool has internet access "completely disabled" and cannot install packages at runtime; OpenAI's hosted containers have no outbound network access by default, with an org-level network_policy allowlist as an opt-in. Both return output artifacts by reference (Anthropic a file_id via the Files API, OpenAI a container_file_citation) rather than inline (Anthropic code-execution tool docs, https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool; OpenAI shell/container tool guide, https://developers.openai.com/api/docs/guides/tools-shell; both retrieved 2026-09-07) |
| Heavy or multi-tenant workloads, managed scale | A cloud sandbox (E2B, Modal, Daytona) | Per-tenant Firecracker/gVisor isolation codecalc does not claim by default |
| Pure arithmetic or symbolic math, nothing else | A small calculator or SymPy MCP | Lower token cost; none of the 31-language runtime machinery |
| A model to stop _guessing_ numbers, equivalence, and speedups — locally, privately, with graded evidence | codecalc | Exact rationals, verify_translation/verify_optimization, and unenforced/grade honesty — offline, no account |
Do not reach for codecalc if you need multi-tenant or network-exposed isolation (its threat model is explicitly single-operator, local, stdio), if zero-setup convenience matters more than measurement, or if a hosted interpreter already covers your case. It earns its keep only when the *correctness of the claim* — not just "it ran" — is the point.
codecalc setupThe fastest path to a working MCP connection, without reading the rest of this section:
uvx 'codecalc[full]' setup # prints what it would do — nothing on disk changes
uvx 'codecalc[full]' setup --write # applies it: merges your client's config, copies the skill
It detects which MCP client is installed (Claude Desktop, Claude Code, Cursor, VS Code, Zed — pass --client=NAME if none or several are found), reuses codecalc doctor's own backend/extras/grammar-cache checks, prints the exact config block in that client's own JSON shape with absolute paths already filled in, runs two real canaries (execute_code, evaluate_expression) to prove the connection would work, and ends in one verdict: ready / degraded / not-ready. --write is the only mode that changes anything — it MERGES the codecalc entry into your existing client config (every other server stays exactly as it was) and backs up the original to .codecalc-bak first. codecalc --help lists every subcommand.
[!NOTE]
Published as `codecalc` 0.13.0 on PyPI (
pip install codecalc) and the
`codecalc-exec` 0.13.0 executor on crates.io
(#91). Every release
artifact carries a keyless sigstore build-provenance attestation — verify
one with
gh attestation verify --repo The-40-Thieves/codecalc; PyPI
wheels additionally carry PEP 740 attestations.
| Where | What you get | Link |
|---|---|---|
| PyPI | pip install codecalc / uvx codecalc | pypi.org/project/codecalc |
| crates.io | the codecalc-exec Rust executor crate | crates.io/crates/codecalc-exec |
| GitHub Releases | wheels for every platform, the executor binaries, the .mcpb bundle, an SBOM, and SHA256SUMS | github.com/The-40-Thieves/codecalc/releases |
| MCP registry (official) | the io.github.The-40-Thieves/codecalc server entry server.json publishes to | registry.modelcontextprotocol.io |
| Smithery | hosted listing and one-click client config | smithery.ai/servers/@The-40-Thieves/codecalc |
| Glama | hosted listing and the score badge above | glama.ai/mcp/servers/The-40-Thieves/codecalc |
| MCPB (Claude Desktop) | the drag-and-drop bundle, attached to every GitHub Release | see GitHub Releases, above |
| Docker MCP Catalog | the mcp/codecalc image Docker builds from this repo's docker/mcp-server.Dockerfile, for Docker Desktop's MCP Toolkit (docker mcp server enable codecalc) | submitted as docker/mcp-registry#5025; listed at hub.docker.com/mcp/server/codecalc once merged |
Not yet listed: PulseMCP and mcp.so do not carry a codecalc entry yet; the Docker MCP Catalog entry is pending review. PulseMCP's own submission page (checked 2026-09-08) says it is not accepting new submissions and that publishing to the official MCP registry — already done, row above — is what it indexes from once submissions reopen, so there is nothing to submit there today. mcp.so takes a submission through its own form. See docs/distribution.md for the exact steps, kept there rather than here because submitting is an action for whoever runs it, not a fact about the current release.
The published install (simplest — no build step, and what most people want):
uvx 'codecalc[full]' # run it directly, no environment to manage
pip install 'codecalc[full]' # into your own virtualenv
From source, if you would rather build the executor yourself:
git clone https://github.com/The-40-Thieves/codecalc
cd codecalc uv sync --all-extras # or: pip install -e '.[full]' cargo build --release --manifest-path executor/Cargo.toml mkdir -p bin # bin/ is gitignored, so a fresh clone has none cp executor/target/release/codecalc-exec bin/ uv run codecalc doctor # verify: backend should read rust
Without the cargo build, everything still runs on the pure-Python fallback — doctor will say so, and the network table below says what that costs.
Why `[full]`. The base install is the MCP surface and the sandbox executor: 31 language runtimes, sessions, packages, ~32 MB. The symbolic half — sympy and z3 — is 88.6 MB measured, and a caller who only runs code should not download an SMT solver to do it. So it is an extra:
| install | size | what you get |
|---|---|---|
codecalc | ~32 MB | execute_code, sessions, packages, complexity-free tools |
codecalc[symbolic] | +83 MB | evaluate_expression, solve, limits, truth tables, z3, units |
codecalc[parsing] | +5 MB installed, +89 MB fetched on first use | analyze_complexity via tree-sitter |
codecalc[full] | ~120 MB | everything |
Nothing fails silently: a tool whose extra is missing returns {"ok": false, "error": "sympy is not installed. It ships in the 'symbolic' extra: pip install 'codecalc[symbolic]' ..."}, and codecalc doctor lists which extras are present before you make a call.
Four names for the capability sets above, plus the two that live outside pyproject.toml entirely — a Docker image and an opt-in isolation boundary. The invariant that makes "edition" a meaningful word here: in the edition that lists a tool, that tool is functional — never listed-but-missing-its-extra. A tool an edition doesn't have returns the dependency_missing contract error naming the extra that provides it (see above), not a silent failure or a tool that appears to exist and doesn't work.
| Edition | Install | What you get |
|---|---|---|
| Full | uvx 'codecalc[full]' / pip install 'codecalc[full]' | The recommended local product: the native (Rust) executor, symbolic tools (evaluate_expression, symbolic, z3_check, …), and parsing (analyze_complexity). Everything this README documents actually runs. |
| Core | uvx codecalc / pip install codecalc | Execution + non-symbolic tools only — the base install in the table above. Every symbolic/parsing tool is still *listed* by tools/list (MCP doesn't support per-install schemas), but calling one returns dependency_missing naming the extra, before any other work happens. |
| Docker | docker build -f docker/mcp-server.Dockerfile . | The MCP server itself, packaged to run as an ordinary container. Core-shaped by default: ships python3/node/ruby/php/perl/gawk/lua/c/cpp/jq/sqlite3 and the default rlimit sandbox — symbolic/parsing are absent by design (no [full] in the base image; see the Dockerfile's own comment for why, including an arm64 z3-solver wheel gap). --build-arg CODECALC_EXTRA=full adds them. This image cannot nest the Strict Host boundary below inside itself (no privileged docker-in-docker), and codecalc doctor inside it says so rather than claiming a boundary it doesn't have. |
| Strict Host | opt-in; CODECALC_STRICT_URL (client) or the gVisor+Docker host itself (server) — see docs/deployment/README.md | Not an install, a *boundary*: the gVisor runsc sandbox on Linux, or AppContainer hardening on Windows, layered above whichever install above is already running. Fails closed — no digest pinned, no fallback to unenforced
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