keepitalive-mcp
MCP server for KEEPitALIVE monitoring, enabling AI agents to read and manage monitors, incidents, triggers, and notification routing via the v1 API, with scope-based tool filtering and safety modes.
README
KEEPitALIVE MCP server
Lets an AI agent read and manage your monitoring through the Model Context Protocol — "is anything down?", "why did the nightly backup fail?", "pause the staging monitors while I deploy".
It is a thin adapter over the public v1 API. Everything it can do, your API key could already do; nothing here bypasses a scope.
Install
Add this to your MCP client's config (Claude Desktop:
claude_desktop_config.json; Claude Code: .mcp.json):
{
"mcpServers": {
"keepitalive": {
"command": "npx",
"args": ["-y", "@keepitalive/mcp"],
"env": {
"KEEPITALIVE_API_KEY": "kia_live_...",
"KEEPITALIVE_MCP_MODE": "write",
"KEEPITALIVE_MCP_TOOLS": "",
"KEEPITALIVE_MCP_EXCLUDE_TOOLS": ""
}
}
}
}
Only KEEPITALIVE_API_KEY is required; the rest are shown with their defaults
so you can see what is adjustable. MODE picks how much the agent may do,
and TOOLS/EXCLUDE_TOOLS narrow the surface further — all three are
described under Configuration.
Create the key under Settings → API access. Grant it the narrowest set of scopes the work needs — see below.
What it exposes
One tool per v1 endpoint: monitors, checks, incidents, comments, triggers and their delivery logs, groups, status pages, notification settings, and the account activity feed.
Two endpoints are deliberately absent. /events is an open-ended SSE stream,
which does not fit a request/response tool — poll get_status or
list_open_incidents instead. Channel setup is app-only, so the notification
tools here read channels and set per-monitor routing but cannot add a
destination.
Rate limits
The API allows 60 requests/minute per key. A 429 is absorbed here rather than
shown to the model: the server sends Retry-After, so the client waits that long
and retries once. Only a repeated 429 surfaces, and its message says plainly
that this is a client-side throttle rather than a problem with the monitored
services — an agent that reads "rate limited" as an outage will report one.
A wait longer than 20 seconds is reported instead of slept through, so a tool call never looks like a hang.
An agent auditing a large fleet can still hit the limit: prefer get_summary
and get_status, which answer fleet-wide questions in one request, over
get_monitor per monitor.
Retries and idempotency
Every POST goes out with an Idempotency-Key. A key generated per request only
covers a retry within that one call — if the model re-invokes a tool after a
timeout, that is a new call with a new key, and the server has nothing to match
it against. Pass idempotency_key explicitly and reuse the same value across
the retry to get real replay: the server returns the first response instead of
creating a second monitor or incident. Reusing a key with a changed payload is
rejected rather than silently replayed.
Which of those actually appear is decided by four layers, each able only to remove:
-
Your key's scopes. The server reads them from
/api/v1/meat startup and registers only tools the key can use. A read-only key gets a read-only server, whatever else is configured.It gates on
effective_scopes— the granted scopes plus everything they imply — so atriggers:writekey also gets the trigger read tools, matching what the API will actually authorize. -
Mode (
KEEPITALIVE_MCP_MODE) —read,write(default), orfull. -
Allowlist (
KEEPITALIVE_MCP_TOOLS) — when set, only these tool names. -
Denylist (
KEEPITALIVE_MCP_EXCLUDE_TOOLS) — removed last.
The effective surface can never exceed what the key already grants, so narrowing here is extra safety rather than the only safety.
Why full is separate from write
write covers everything reversible: creating and editing monitors, pausing and
resuming, opening and closing incidents, toggling triggers. The delete_* tools
are withheld until you set mode=full.
A key wide enough to delete is not by itself a decision to let an agent delete —
those tools remove data with no undo, and an agent reaching for one after
misreading a situation is the failure worth designing against. Deleting a
monitor also takes its history with it; pausing (set_monitor_active) is almost
always what was actually meant.
Configuration
| Variable | Default | Purpose |
|---|---|---|
KEEPITALIVE_API_KEY |
— | Required. Your kia_live_… key. |
KEEPITALIVE_MCP_MODE |
write |
read, write, or full. |
KEEPITALIVE_MCP_TOOLS |
— | Comma-separated allowlist of tool names. |
KEEPITALIVE_MCP_EXCLUDE_TOOLS |
— | Comma-separated denylist. |
Read-only example:
{
"env": {
"KEEPITALIVE_API_KEY": "kia_live_...",
"KEEPITALIVE_MCP_MODE": "read"
}
}
On startup the server writes its resolved plan, scopes, mode, and every withheld tool with the reason to stderr — your MCP client's log shows exactly what the agent can and cannot do.
Troubleshooting
could not reach …/me — the server checks the key before serving anything,
so this is a bad or revoked key, or no route to the API. It
fails at startup deliberately: a key problem discovered on the first tool call
gets reported by the model as a monitoring outage.
A tool you expected is missing — read the stderr banner. It names the reason, usually a scope the key was not granted.
Development
npm install
npm test # unit + stdio integration tests against a stub API
npm run build
The tests spawn the real server over stdio and talk to it with the MCP client, against a local stub of the API — no KEEPitALIVE instance required.
The contract
The tool schemas are checked against the API's published OpenAPI document,
fetched from https://keepitalive.dev/api/openapi.json when the tests run.
That is what stops this server offering a field the API would reject — the bugs
that check has caught were all of that shape: a field that looked plausible and
was accepted by nothing.
Because it is fetched rather than vendored, a field added or removed on the API fails the suite the same day instead of whenever someone remembers to refresh a copy. The cost is that these tests need network access. To check against a pre-release deployment:
KEEPITALIVE_CONTRACT_URL=https://example.com/api/openapi.json npm test
推荐服务器
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。
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。
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。
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。
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。
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。
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。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。
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。