Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Issue tracker and durable project memory for your coding agent, scoped to the folder you open.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
TRAIL_API_URL, TRAIL_PROJECT你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"trail": {
"command": "npx",
"args": [
"-y",
"usetrail"
],
"env": {
"TRAIL_API_URL": "your-value",
"TRAIL_PROJECT": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add trail --env TRAIL_API_URL=your-value --env TRAIL_PROJECT=your-value -- npx -y usetrail运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"trail": {
"command": "npx",
"args": [
"-y",
"usetrail"
],
"env": {
"TRAIL_API_URL": "your-value",
"TRAIL_PROJECT": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"trail": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"usetrail"
],
"env": {
"TRAIL_API_URL": "your-value",
"TRAIL_PROJECT": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
TRAIL_API_URL envTRAIL_PROJECT env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
发布者(usetrail.dev)不是仓库所有者(arsprengel);可能是其他人代为上架的
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 0 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.29.1)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
The MCP server for [Trail](https://usetrail.dev) - it gives your coding agent your team's tracker and the project's durable memory, scoped to the folder you have open.
Your agent stops working blind. It reads what is open, writes what it did, and records what it learned the hard way - in the same board your team looks at, not in a scratch file that only that session can see.
This package is the client. It talks to your Trail account over the REST API; there is no database here. Dependencies are @modelcontextprotocol/sdk, zod and the built-in fetch.
# 1. connect this machine to your Trail account (opens your browser; no token to copy)
npx -y usetrail@latest login
claude mcp add trail -s user -- npx -y usetrail@latest
No account yet? Create one at usetrail.dev - the free plan covers three projects and three people.
Not tied to one agent. This is a plain MCP server over stdio, speaking the three protocol versions in use today. Any MCP-compatible client can run it; only the registration command differs, and each client documents its own.
Prefer a local checkout? Clone it instead, and the connector keeps itself up to date on its own (see Updating):
git clone https://github.com/arsprengel/trail-mcp.git ~/.trail-mcp
npm --prefix ~/.trail-mcp install --omit=dev node ~/.trail-mcp/bin.js login claude mcp add trail -s user -- node ~/.trail-mcp/bin.js
On Windows, write the real path instead of ~: the registration stores the argument verbatim and Node does not expand it.
The tracker - list_items, get_item, add_item, update_item, move_item, get_next, delete_item. Real items on your team's board, with type, status, priority, assignees and history. Work the agent picks up shows as in progress and closes when it is done, so the board is not a story someone has to retell.
Project memory - list_memory, get_memory, add_memory, update_memory, review_memory. The durable knowledge of a project: the deploy that has a trap in it, the decision nobody should relitigate, the command that is not in any README. Shared by everyone on the project, human or agent. A new session reads it before acting instead of rediscovering it.
Reminders - add_reminder, list_reminders, update_reminder, delete_reminder. Things with a date that must not be silently missed - and the agent closes or reschedules them itself once the date arrives.
Attachments - add_attachment, get_attachment. Files that belong to an item.
Trail holds many projects. The connector uses the folder you have open as the project - the folder name, or whatever a .trail file next to it says, or the TRAIL_PROJECT variable. So the agent writes to the right project without being told, and two repositories never bleed into each other. To reach across, the agent passes project explicitly on the tool.
Installed for you, once, when you log in - and on the connector's first run, which covers people who paste a token into the registration instead of logging in through the browser. Every session that starts in a project with a tracker gets the open items and the project memory in context from the first message - the agent starts knowing, instead of depending on someone remembering to ask. Fail-silent by design: no login or no network and the hook stays quiet.
Each tool keeps this in its own place, so the connector writes to the one that tool actually reads:
~/.claude/settings.json.~/.gemini/settings.json.~/.gemini/config/mcp_config.json, which is the file it actually reads: `gemini mcp add` does not connect Trail in Antigravity, because that command writes to ~/.gemini/settings.json instead. Field-tested on Antigravity 2.11 (Windows), of the three things that live in that config folder only the tool registration is honored - the opening-summary hook is not run, and a plugin folder placed exactly where its own documentation says is not read. So on Antigravity the agent gets the tools and the server's working rules, and fetches the project summary itself on the first turn instead of receiving it. The plugin path is built and tested behind a switch, waiting for that to change.usetrail status tells you, per tool found on this machine, whether it is on - so you never have to go digging through settings files to find out.
It never fights you. A tool that isn't on the machine gets nothing created for it, and hooks install will not fabricate a config for a program you don't have. If you remove it with hooks uninstall, it does not come back on its own - put it back with:
npx -y usetrail@latest hooks install
Clients without a hook mechanism are covered too: the server's own instructions tell the agent to fetch the summary itself before acting.
Point it at your server once and it stays pointed:
TRAIL_API_URL=https://your-trail npx -y usetrail@latest login
Without that variable, login goes to the hosted service.
npx -y usetrail@latest # start the MCP server over stdio - what your agent runs
npx -y usetrail@latest login # connect this machine (browser confirmation, no token copying) npx -y usetrail@latest status # server address, current project, whether a token is present npx -y usetrail@latest logout # delete the saved token npx -y usetrail@latest doctor # find the install on this machine and unstick auto-updates
The token is stored at ~/.config/trail/token.json (mode 600). Revoke it whenever you want from the "AI token" panel in the app.
| Variable | What it does |
|---|---|
TRAIL_API_URL | Your own Trail's address. Saved after the first login. Defaults to the hosted service. |
TRAIL_PROJECT | Forces the project name (default: the open folder). |
TRAIL_API_TOKEN | A token directly, skipping the browser login. Useful in CI. |
TRAIL_LANG | Language of what the connector prints on screen: pt or en. Without it, your system's language, and English when your system states none. Never changes what the agent reads. |
The former names (TETHER_*) keep working, with no deadline - the product used to be called Tether, and nothing anyone already configured has to change.
The `@latest` is not decoration. Registered as npx -y usetrail@latest, the connector resolves the published version every time your agent starts a session, so a new release reaches you without anyone doing anything. Registered as bare npx usetrail, npm reuses whatever copy it cached the first time - and that machine stays on that version forever. Use @latest.
The cost of @latest is about a second at session start, and a network call. Neither matters in practice: the connector talks to Trail over the network anyway.
A cloned install updates itself differently: on startup it fires a quiet fast-forward pull in the background, at most every six hours. The running session keeps the version it loaded; the next one starts updated. No network or a local conflict and nothing happens, silently.
Stuck on an old version? npx -y usetrail@latest doctor finds the install, reports the version and unsticks it.
Node 18 or newer.
MIT. This package is the client - the connector you run on your own machine. Use it, fork it, adapt it. The Trail service it talks to is a separate, hosted product.