tai-skeleton

tai-skeleton

Open-source MCP server hosting tools, agents, extensions, connectors, hooks, and storage for the TAI ecosystem, implementing the tai42-contract interfaces.

Category
访问服务器

README

tai42-skeleton

CI License: Apache 2.0

The open-source implementation of tai42-contract — the extended MCP server that hosts tools, agents, extensions, connectors, hooks, and storage for the TAI ecosystem. It provides the concrete TaiMCP server and the runtime engines (tool registry and adapters, the agent registry, the OAuth connector engine, the access-control middleware, the hooks router, the template/storage manager, the manifest loader, and the transport layer) that implement the protocols declared in tai42-contract.

Providers — OAuth connectors, storage backends, config providers, worker backends, monitoring — ship as separate plugins that register through the tai42_app contract handle when the manifest loads them; no plugin imports the skeleton.

Position in the ecosystem

TAI is an open-source runtime for MCP tools, agents, and workflows — the server that hosts a capability and supplies the operational layer around it (manifest loading, access control, OAuth connectors, background execution, monitoring, storage, and human-in-the-loop steps).

Three packages; each depends only on the ones to its left:

tai42-contract  <--  tai42-kit  <--  tai42-skeleton
(interfaces)      (helpers)     (the server)

tai42-skeleton is the server at the end of the chain: it depends on only tai42-contract (the pure interface package) and tai42-kit (generic leaf helpers). It is the runnable body every plugin plugs into.

Install

Requires Python 3.13+. Nothing is on PyPI yet, so install from source: clone the three repos side by side — this repo's [tool.uv.sources] points at ../tai-contract and ../tai-kit:

git clone https://github.com/tai42ai/tai-contract
git clone https://github.com/tai42ai/tai-kit
git clone https://github.com/tai42ai/tai-skeleton
cd tai-skeleton
uv sync

Add the toolbox extra for batteries — it pulls in the tai42-toolbox contrib package, whose composition tool extensions (chain, batch) and generic tool collection load from the manifest (see examples/toolbox):

uv sync --extra toolbox

Once the packages are published, pip install tai42-skeleton[toolbox] will work too.

Run it

The hello-world app in examples/hello boots one local greet tool, no external services. From the repo root:

ACCESS_CONTROL_ENABLE=false \
PYTHONPATH=examples/hello \
uv run tai serve --manifest-path examples/hello/manifest.yml --port 8765

The MCP endpoint is then http://127.0.0.1:8765/mcp. tai backend runs the agent/worker backend process and tai metrics serves the Prometheus endpoint; the full command surface is in the CLI reference below.

tai serve, tai backend, and tai metrics form one run family over a single shared Prometheus multiprocess directory (PROMETHEUS_MULTIPROC_DIR, default a fixed absolute path under the host temp dir; any override must be absolute). Point all three at the same directory and restart them together: tai serve clears the directory once at boot, so restarting it mid-run while a backend worker is still writing orphans that worker's counters until the whole family restarts. examples/README.md walks through the examples, and examples/manifest.yml is the commented manifest reference.

The worker bus

When a deployment runs more than one process — several tai serve workers, a tai backend runtime alongside the server, or multiple pods sharing one config — a manifest edit on one process must reach the others, or siblings serve stale state. The worker bus is how every process converges: each subscribes to one Redis channel at startup, a mutation applies locally and is then broadcast, and the response carries a per-origin report of how every worker fared.

The bus is internal app infrastructure, like the reload gate — it is NOT a plugin. Nothing about it is registrable, swappable, or user-selectable; there is exactly one bus and no manifest field chooses an implementation. It is configured only by environment: set TAI_BUS_REDIS_URL (plus the optional TAI_BUS_* knobs) to turn it on. A single-worker, file-mode, no-backend deployment needs no bus and runs on a no-op local variant; a multi-worker, backend-bearing, or k8s-mode boot refuses to start without one, naming TAI_BUS_REDIS_URL. On a shared Redis, TAI_BUS_NAMESPACE must diverge per stack — Redis pub/sub is server-global, so co-tenant deployments would otherwise cross-talk.

Development

The dev venv resolves tai42-contract, tai42-kit, tai42-toolbox, and tai42-identity-redis from sibling checkouts (see [tool.uv.sources]):

uv sync --extra dev
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv run pytest -q

See CONTRIBUTING.md for the rules.

Documentation

The whole platform — the quickstart, concepts, guides, and the generated reference — lives in the unified documentation site:

  • Getting started & install: https://tai42.ai/getting-started/installation
  • Quickstart: https://tai42.ai/getting-started/quickstart
  • Concepts: https://tai42.ai/concepts
  • Guides: https://tai42.ai/guides
  • Built-in tools & extensions: https://tai42.ai/concepts/tools-and-extensions
  • Reference (HTTP API, CLI, Python SDK): https://tai42.ai/reference/cli

License

Apache-2.0. See LICENSE and NOTICE.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选
mcp-server-qdrant

mcp-server-qdrant

这个仓库展示了如何为向量搜索引擎 Qdrant 创建一个 MCP (Managed Control Plane) 服务器的示例。

官方
精选
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选