tasks-mcp

tasks-mcp

An MCP server that exposes outputty's task tracker as typed tools backed by GitHub Issues, allowing coding agents to manage tasks with two-way sync.

Category
访问服务器

README

@outputty/tasks-mcp

A local MCP server that gives a coding agent a dependency-aware task tracker. The agent calls typed tools — add_task, list_ready, prereqs, blockers, sync — over a task graph that is mirrored two-way into GitHub: one issue per task, field:value labels for its execution properties, and a Projects v2 kanban board.

Tasks live in a stack of provider layers: a local file on top (every read is instant and offline), GitHub beneath it as the source of truth. Deleting the local file loses nothing — sync rebuilds it.

tasks-mcp architecture: the provider stack

Install

No clone. Add the server to your project's .mcp.json and your MCP client launches it on demand:

{
  "mcpServers": {
    "tasks": { "command": "npx", "args": ["-y", "@outputty/tasks-mcp"] }
  }
}

Requirements: Node ≥ 18 (or bun), a repo with a github.com origin remote, and either gh logged in or GITHUB_TOKEN set. For the kanban board, grant the token the project scope once: gh auth refresh -s project (without it, tasks still land as issues and the board is skipped with a warning).

The two questions it answers

The tracker is a dependency graph, and the two questions a graph is FOR each have a dedicated tool.

"I want to start on task X — what has to be done first?"

// tool: prereqs        { "project": "/abs/repo", "id": "deploy" }
{
  "id": "deploy",
  "startable": false,
  "order": [["schema"], ["api", "infra"]], // finish layer 1, then layer 2, then start deploy
  "tasks": [
    {
      "id": "schema",
      "status": "open",
      "deps": [],
      "summary": "Design the schema",
      "tier": 3,
      "qa": "subagent",
      "priority": "normal",
    },
    {
      "id": "api",
      "status": "open",
      "deps": ["schema"],
      "summary": "Build the API",
      "tier": 2,
      "qa": "inline",
      "priority": "high",
    },
    {
      "id": "infra",
      "status": "open",
      "deps": [],
      "summary": "Provision infra",
      "tier": 3,
      "qa": "subagent",
      "priority": "normal",
    },
  ],
}

startable: true with an empty order means nothing is in the way — start now. Done tasks never appear: a finished dependency ends the chain.

"What is the biggest blocker right now?"

// tool: blockers       { "project": "/abs/repo" }
{
  "blockers": [
    {
      "id": "schema", // the single biggest bottleneck: first entry, most waited-on
      "summary": "Design the schema",
      "priority": "normal",
      "blocks": 3, // how many open tasks transitively wait on it
      "blocked": ["api", "ui", "deploy"],
      "highPriorityBlocked": ["ui"], // how it aligns with priorities
      "unblockedBy": [], // the path to it, dependency-ordered (empty: work it now)
    },
    {
      "id": "infra",
      "summary": "Provision infra",
      "priority": "normal",
      "blocks": 1,
      "blocked": ["deploy"],
      "highPriorityBlocked": [],
      "unblockedBy": [],
    },
  ],
}

Read it top to bottom: the first entry unblocks the most work; unblockedBy is what has to happen to even get to it; highPriorityBlocked shows whether clearing it serves the priorities.

The tools

Every tool takes project — the absolute path to the repo it acts on — because the server has no working directory of its own. Reads are answered from the local file layer and never touch the network; the first GitHub-touching call (a write, or sync) resolves repo, credentials, labels, and the board once.

Tool Answers Writes
prereqs what must be done before this task can start, in build order —
blockers which tasks hold up the most work, ranked —
list_ready which tasks can be worked right now (open, settled, deps done) —
list_planning which tasks planning still owns (drafting / replan) —
schedule the whole open plan as dependency layers; errors on a cycle —
get_task one task's full record —
add_task create a task (file + issue + labels + board card) ✎
amend_task widen an open task's scope, or set its brief ✎
close_task mark done (closes the issue, moves the card) ✎
get_trail a task's trail: its issue comment thread, oldest first —
append_trail append one entry to a task's trail (posts an issue comment) ✎
sync reconcile every layer both ways; adopt hand-opened issues ✎

A task carries: id, title, status, deps, scope, the execution-modifying properties tier (1–4), qa (skip/inline/subagent), priority (high/normal/low), spec, stage, kind, and brief/contract prose. On GitHub, the scalar properties are worn as field:value labels (tier:2, priority:high, …) — visible, filterable, and editable in the GitHub UI; edit a label there and sync pulls the change back. See docs/architecture.md for the full mapping.

Trails — the decisions behind a task

A task's trail is its GitHub issue comment thread. append_trail posts a comment; get_trail reads the whole thread back, oldest first — so the decisions and actions behind a task live right on the issue, and every comment counts, including ones people write by hand.

// append_trail  { "project": "/abs/repo", "id": "readme-prereqs-order",
//                 "kind": "decision", "note": "prereqs example outputs [[schema],[api,infra]]",
//                 "link": "README.md:42" }
{
  "trail": [
    {
      "kind": "decision",
      "note": "prereqs example outputs [[schema],[api,infra]]",
      "link": "README.md:42",
      "author": "octocat",
      "at": "2026-08-17T19:30:00Z",
    },
  ],
}

note is the comment body; author and at come from GitHub. kind (decision / action / note) and link are optional — outputty tucks them into a hidden marker on the comments it writes, so the comment still renders as plain text on GitHub while round-tripping the tags. A comment a person leaves by hand has no kind/link, just its note, author, and at. Trails need a GitHub-backed project; append_trail requires the task's issue to exist (sync it first).

Configuration

Preferences are configured centrally, through the server itself — the set_config tool writes them, they are stored beside the task caches (never in your repo), and they propagate to every provider layer:

// set_config — a global spec that applies to every repo…
{ "project": "/abs/repo", "scope": "global", "config": { "labels": true, "board": "Tasks" } }
// …overridable per repo:
{ "project": "/abs/repo", "scope": "repo", "config": { "labelFields": ["tier", "priority"] } }

Precedence, weakest first: defaults < CLI flags < global spec < per-repo override. get_config shows every layer plus the effective result. Configurable: provider, projects, projectNumber, board, labels (label sync on/off), labelFields (which properties become labels). Everything is zod-validated — a typo'd key or mistyped value fails loudly, naming the file.

Deployment flags (in .mcp.json's args, e.g. ["-y", "@outputty/tasks-mcp", "--no-projects"]):

Flag Description Default
--http / --port <n> standalone HTTP server instead of stdio stdio, 3917
--provider <name> the remote layer backing each project github
--project-number <n> target an existing Projects board find/create
--no-projects disable the board sync board on
--board <title> board title to find/create Tasks
--cache-dir <dir> where the file layer + config live OS cache dir

Credentials come from GITHUB_TOKEN / GH_TOKEN, else gh auth token.

More

  • Architecture — the provider stack, sync semantics, and the task ↔ GitHub mapping (body block, labels, board).
  • CLI — the same tracker as shell commands (tasks-mcp blockers), no MCP involved, plus the library API.
  • Development — building, testing, and releasing this package.

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
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
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
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
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
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
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
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