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)
Stdio MCP bridge for SandBase Harness agents, sessions, turns, artifacts, and cancellation.
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。
MANAGED_AGENTS_URL, MANAGED_AGENTS_API_KEY「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
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": {
"sandbase-harness": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"MANAGED_AGENTS_URL",
"-e",
"MANAGED_AGENTS_API_KEY",
"ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8"
],
"env": {
"MANAGED_AGENTS_URL": "your-value",
"MANAGED_AGENTS_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add sandbase-harness --env MANAGED_AGENTS_URL=your-value --env MANAGED_AGENTS_API_KEY=your-value -- docker run -i --rm -e MANAGED_AGENTS_URL -e MANAGED_AGENTS_API_KEY ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"sandbase-harness": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"MANAGED_AGENTS_URL",
"-e",
"MANAGED_AGENTS_API_KEY",
"ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8"
],
"env": {
"MANAGED_AGENTS_URL": "your-value",
"MANAGED_AGENTS_API_KEY": "your-value"
}
}
}
}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": {
"sandbase-harness": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"MANAGED_AGENTS_URL",
"-e",
"MANAGED_AGENTS_API_KEY",
"ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8"
],
"env": {
"MANAGED_AGENTS_URL": "your-value",
"MANAGED_AGENTS_API_KEY": "your-value"
}
}
}
}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 sandbaseai 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。
入力が必要なもの:MANAGED_AGENTS_URL, MANAGED_AGENTS_API_KEY
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(0.3.8)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Dockerも必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, Georgia MCP
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.
An MCP server for Georgian public data, read from the organizations that publish it.
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。
English | 中文
AI-readable project metadata: llms.txt · installation guide
A local-first runtime for AI agents. Sessions, sandboxed tools, memory, credentials, audit trails, and a built-in Console — all running on your machine or in your own infrastructure.
Agent SDKs handle the model loop. Production agents need more: persistent sessions, tool governance, sandbox boundaries, credential handling, memory, auditability, and a UI for humans to inspect what happened. managed-agents is that runtime layer — not a visual workflow builder and not another model SDK.
| Need | What Harness provides |
|---|---|
| Run generated code safely | Local, Docker, Kubernetes, and self-hosted worker sandboxes |
| Inspect long-running agents | Persistent sessions, resumable event streams, audit, and replay |
| Control tool access | MCP toolsets, credential vaults, permission policies, and approvals |
| Operate any model | OpenAI, Anthropic, MiniMax, and OpenAI-compatible providers, including DeepSeek V4 |
| Keep infrastructure yours | Local-first SQLite and file storage with no required hosted control plane |
/v1 API and local Consolemanaged-agents/sdknpm run release:checkRequirements: Node.js 22+, npm 10+, and a model provider API key (OpenAI, Anthropic, MiniMax, or any OpenAI-compatible endpoint). Docker is optional and only needed for Docker-backed sandboxes.
git clone --branch v0.3.8 --depth 1 https://github.com/sandbaseai/sandbase-harness.git
cd sandbase-harness npm ci npm run build mkdir ../my-agents && cd ../my-agents node ../sandbase-harness/dist/index.js init node ../sandbase-harness/dist/index.js start
init writes a workspace into the directory you run it from: an agent, a skills folder, and config.yaml, whose provider reference is the ${OPENAI_API_KEY} environment variable. start serves the API and the Console on http://127.0.0.1:3000.
Two steps finish the setup, both on Settings > Setup at http://127.0.0.1:3000/dashboard:
start command again, or use the restart button. A saved setting only takes effect at startup. If your provider is not in the list, choose the OpenAI-compatible vendor and set its base URL.deepseek-chat for DeepSeek, for example. An agent carries its own model ID, so the gpt-4o that init writes is not valid for every provider, and a wrong ID fails the turn with model_not_found.Send the first message from the Console: open Sessions, create a session for the agent, and type into the composer. From a terminal it is one command:
node ../sandbase-harness/dist/index.js chat agent_assistant --message "hello" --tool-approval allow
chat sends that one message and exits once the turn settles; without --message it keeps the session open and streams until you interrupt it. --tool-approval allow preauthorizes the tool calls the agent may make, which the init template otherwise parks for approval and waits for a person to answer; see CLI.
The unscoped managed-agents name on npm is not this project. Until an official scoped package is announced in this repository, install only from the tagged GitHub source release shown above. Do not run npx managed-agents or npm install managed-agents.
The included development container installs dependencies and builds the runtime. When the terminal is ready, start the server on the forwarded port:
node dist/index.js start --host 0.0.0.0
Open the forwarded SandBase Harness Console port, then configure a model in Settings > Setup. Codespaces usage may be billed by GitHub; the local quick start above remains free and keeps all runtime data on your machine.
| Console overview | Settings | API reference |
|---|---|---|
The runtime answers its own /v1 API on that same port, and an official Anthropic TypeScript SDK client drives it unchanged: point the client's baseURL at the runtime, give it the runtime API key, and the quickstart in examples/official-sdk runs a whole turn — message, tool call, tool result, final reply — against it. That example is executed on every pull request by tests/conformance/official-sdk-quickstart.test.ts, so the compatibility it describes is compatibility that is tested rather than claimed.
The same surface is specified in docs/api.md, and this repository's own TypeScript SDK is documented under SDK below.
Coverage of the published Claude Managed Agents contract is declared entry by entry in src/core/capabilities/matrix.ts: of the official SDK's route surface (110 method/path pairs as of SDK 0.132.1), 90 routes are mounted and 20 refuse by name; no route is deferred. Partial and Unsupported entries always name their reason.
The table below is generated by npm run docs:compat, and a contract-honesty test fails when it drifts from the matrix.
Full compatibility table (generated)
| Area | Official capability | Status | Notes |
|---|---|---|---|
| headers | compatibility-header-admission | Partial | Version, beta, and mutual-exclusion rules are enforced for any request that carries a compatibility header. A request with no compatibility header is accepted as a local caller, which the published contract does not define; this header-free path is a deliberate local-first extension for a self-hosted single-tenant runtime, recorded as such in the headers contract §4, and it is why this entry stays partial. |
| headers | extension-namespace-exclusion | Supported | |
| pagination | opaque-cursors | Partial | A collection's envelope follows the mount: the operations router serves /v1 with {data, prev_page, next_page} and its /v1/x mirror with the local {data, has_more, first_id, last_id}, chosen through one pager so no handler emits both spellings, and cursors that are readable base64url JSON rather than opaque binary. Every canonical /v1 collection serves the canonical envelope, with no exceptions: the complete-set listings carry {data, prev_page: null, next_page: null} and the windowed ones carry a followable cursor — /v1/sessions pages by number under {order, created_at bounds, page} and rejects a cursor replayed under another ordering or creation window, /v1/sessions/{id}/events carries {order, filter, after_id}, /v1/skills and the credential audit listings use {offset, filter} — so a cut page says so instead of looking complete. The one listing that is neither shape is /v1/environments/{id}/work-items, a windowed extension that adds a counts object and is named in the contract. |
| pagination | cursor-query-binding | Supported | |
| errors | structured-error-envelope | Supported | |
| agents | agent-crud | Supported | |
| agents | model-object-profile | Partial | String and object model forms parse field by field. effort and speed are stored, returned by the read projection (the agent read, the version read, and the session snapshot), and executed on the Anthropic provider under a model capability table — effort becomes output_config.effort, fast becomes speed: "fast" with the fast-mode beta, and adaptive-thinking models receive thinking: {type: "adaptive", display: "omitted"}; a listed model refused a level or speed it cannot take fails admission, an unknown model id or non-Anthropic provider sends nothing, and a deployment's own reasoning_effort model setting is operator-level and separate. inference_geo is refused by name with unsupported_model_field because this runtime has no inference-geography control; and a canonical multiagent roster is refused by name rather than executed. |
| agents | multiagent-roster | Unsupported | In scope but not implemented (decisions/0002): a canonical multiagent block (multiagent_20261001 or any other shape) is refused by name on both agent create and agent update, because no thread, subagent, workflow, or advisor surface exists to honour it and the surface is a protocol of its own this runtime does not implement; accepting it would let a caller believe delegation or workflow runs are in effect. Local delegation is registered separately as an extension. |
| agents | local-delegation-subagent | Supported | |
| sessions | session-lifecycle | Supported | |
| sessions | initial-events | Supported | |
| sessions | prompt-caching | Supported | |
| sessions | session-update | Supported |
| budget | session-budget | Partial | Consumption is priced in integer microcents from the append-only log, and a session may declare a max_list_cost ceiling at creation. The builtin loop checks the ceiling inside a turn: the step that crossed the cap is the last one, the session idles with stop_reason budget_reached and a session.usage immediately before it, and an accepted budget update or removal resumes the session on its own — a tool call the ceiling stranded is settled so the resumed turn sees a paired transcript. At the ceiling the next work-starting event is refused with budget_reached while events that settle work already in flight are still accepted, so the next model request does not start; a declared outcome's revision loop reads the same spend and stops at the ceiling too, closing the outcome with result budget_reached rather than starting another grading p
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