ezstat-mcp-server
This server lets AI agents push metrics to EzStat and read them back, plus manage alerts, all through MCP tools.
track_metric — record counters (default +1) or gauge/value measurements, with optional back-dated timestamps.
ask_ezstat — ask natural-language questions about your metrics and get grounded answers with supporting data.
read_stat — fetch a stat's latest value, recent series, and summary (count/min/max/avg/sum), optionally filtered by time range and rollup resolution.
list_stats — list account metrics (names/types) and optionally filter by counter or value.
create_alert / list_alerts / delete_alert — manage webhook alerts with conditions like threshold, %-change, heartbeat, or sustained.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ezstat-mcp-server What's the error rate for the last 30 minutes?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
EzStat MCP Server
A standalone Model Context Protocol server for EzStat. Let Claude / Cursor / any MCP-compatible agent push and read your metrics natively — no copy-paste, no dashboard hop. This is the product behind the "metrics your agents write and read themselves" positioning: every agent that touches your code can also touch your observability.
The server speaks stdio (the standard transport for local agent runners) and — as a
hosted endpoint at https://mcp.ezstat.dev — streamable HTTP, exposing the same
seven focused tools. Each tool description is written for an agent audience so the model
knows when to call it.
Hosted endpoint — zero install
Agents can connect to the hosted streamable-HTTP endpoint with nothing but an EzStat API key — no npm install, no local process:
{
"mcpServers": {
"ezstat": {
"type": "http",
"url": "https://mcp.ezstat.dev",
"headers": {
"Authorization": "Bearer ezkey_your_api_key_here"
}
}
}
}Multi-tenant: every request carries your key as
Authorization: Bearer <ezkey>; the server holds no key of its own and each connection only ever sees its own account.Discovery is free:
initializeandtools/listwork without a key, so clients and registry inspectors can introspect the tool surface before you configure anything.Tool calls require the key: a call without one (or with an invalid one) returns a clean in-band MCP tool error telling the agent what to send — never a hang or a crash.
Stateless: no sessions are issued or required; each POST is self-contained.
This hosted endpoint also satisfies the requirements for hosted/remote listings on MCP
registries (e.g. Smithery's hosted-server path) — see server.json's remotes entry.
The stdio transport below remains fully supported and is still the right choice when you want the server running locally under your own runner.
Related MCP server: Datadog MCP Server
Tools
Tool | What it does |
| Record a metric point your app "produced" — counter (+N, default +1) or gauge (e.g. 42.5 ms). |
| Ask a natural-language question about your metrics (the agent-read path). |
| Structured read of a stat: latest value + series + summary (count/min/max/avg/sum) for a window. |
| List the account's metrics (names + types + description). |
| Create a webhook alert on a stat (threshold / %-change / heartbeat / sustained). |
| List the account's alerts (id, stat, condition, channel, enabled, last fire). |
| Delete an alert by id. |
Tool descriptions are tuned for agent reasoning — see src/server.ts.
Install (Claude Desktop / Claude Code / Cursor)
One-line install (npm/pnpm)
# pnpm
pnpm add -g ezstat-mcp-server
# npm
npm install -g ezstat-mcp-serverConfigure the MCP client
Claude Desktop (~/.config/Claude/claude_desktop_config.json on macOS/Linux,
%APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": {
"ezstat": {
"command": "ezstat-mcp-server",
"env": {
"EZSTAT_API_KEY": "ezkey_your_api_key_here"
}
}
}
}Cursor (~/.cursor/mcp.json):
{
"mcpServers": {
"ezstat": {
"command": "ezstat-mcp-server",
"env": {
"EZSTAT_API_KEY": "ezkey_your_api_key_here"
}
}
}
}Get your API key from https://ezstat.dev (it's the "ezkey").
Smithery
npx -y @smithery/cli install ezstat-mcp-server --client claudeOr visit https://smithery.ai/server/ezstat-mcp-server and click Install.
About EzStat
EzStat is a dead-simple hosted metrics service: one HTTP POST in, a live chart out. No SDK, no collector daemon, no YAML, no dashboard builder. Counters and value/gauge stats auto-create on first POST; charts, share/embed, and CSV/JSON export are built in, and Ask-Your-Data (live) answers plain-English questions from your own metrics, with every number verified against the retrieved data before it reaches you.
Flat monthly pricing by tracked stats — $19 / $49 / $149 — plus a card-gated free tier (no charge; see your data live before paying). Your data is yours: export any time, cancel any time. Anomaly detection and metric correlations are live. Features that are not shipped yet (alerts, weekly digest) are marked coming soon on the site rather than sold — what you see live is what works.
Coming from StatHat?
If your api.stathat.com calls stopped and your dashboards went dark: EzStat speaks
StatHat's wire format — the same /ez, /c, /v endpoints, same params, same
response. The migration is usually one line:
# before
curl -X POST https://api.stathat.com/ez -d "stat=messages sent" -d "ezkey=KEY" -d "count=1"
# after — change the host, use your EzStat key; stats auto-create
curl -X POST https://api.ezstat.dev/ez -d "stat=messages sent" -d "ezkey=EZSTAT_KEY" -d "count=1"One gotcha, and it is the important one: check the response BODY, not the status code.
Because the wire format is StatHat's, a rejected write (stale key, wrong key, quota hit)
still returns HTTP 200, with the verdict in the body — {"status":200,"msg":"ok"} means
recorded, {"status":"error","msg":"..."} means it was not. An unmodified StatHat client
reports those as successes while your charts keep rendering old data, so nothing looks
broken. New code should send X-EzStat-Strict: 1 on /ez and get real HTTP status codes
instead. (This server already does the right thing: it treats an error body as a failure
regardless of the HTTP code.) After switching, confirm points are arriving with
ezstat.dev/tools/ezstat-verify.py — documented
at ezstat.dev/docs#verify.
Most StatHat client libraries take a base-URL override in one line. Saved a StatHat CSV/JSON export? The importer recreates your stats and backfills history (8 MB / 500k points per file). Full guide: docs/stathat-migration.md · ezstat.dev/migrate/stathat — including the migrator deal: free tier to see it live first, a 12-month price-lock, and white-glove import for the first 25 migrations.
Why agent-native metrics
Your coding agent deploys, tests, and ships — it should also be the one tracking and reading the numbers. That's this server: metrics your agents write and read themselves. The reasoning: docs/agent-native-metrics.md · comparison with StatHat/StatFlow/Datadog: ezstat.dev/vs.
Environment variables
Variable | Required | Default | Notes |
| yes | — | Your EzStat API key (the "ezkey"). Never hardcode. Never log. |
| no |
| Override for staging / self-hosted. |
| no |
| Per-request timeout in milliseconds. |
| no |
| Override if you proxy the EZ endpoint. |
| no |
| Ask-Your-Data path. |
| no |
| Stats list path. |
A starter .env.example is shipped.
How it talks to EzStat
The tools map to real EzStat API routes (all auth by EzStat API key):
Tool | Method | Path | Auth |
| POST |
|
|
| POST |
|
|
| GET |
|
|
| GET |
|
|
| POST |
|
|
| GET |
|
|
| DELETE |
|
|
track_metriccalls the StatHat-compatible EZ ingest ({"ezkey","stat","count"?,"value"?,"t"?}). Counter semantics: omit bothcountandvalueto recordcount=1(counter +1).ask_ezstatcalls Ask-Your-Data ({"query":"..."}→{answer, data, intent, ...}). This is the agent read path — count it as an agent read for usage tracking.read_statreturns the stat's recent series + summary; passfrom/to(Unix seconds, converted to ISO 8601 at the HTTP boundary) to bound the window. The bucket width is auto-derived from the requested span (≤6h → minute, ≤7d → hour, >7d → day).list_statsreturns the account's stats; passtypeto filter tocounterorvalue.
Build & run locally
pnpm install
pnpm build # tsc → dist/
pnpm start # node dist/index.js (stdio MCP server)
pnpm test # vitest run — fully mocked network, no real HTTP
pnpm dev # tsx watch src/index.tspnpm start requires EZSTAT_API_KEY to be set; the server exits with a clear error
otherwise (and never logs the key).
Security
The API key is read only from
EZSTAT_API_KEY— never hardcoded, never printed.All error messages are scrubbed: the API key is never included in tool results.
HTTP errors carry a stable machine-readable
code(unauthorized,rate_limited,timeout,not_found,server_error, ...) and a human-readablemessagesafe to relay.Tests mock the network — no real HTTP leaves the test process.
Registry metadata
For Smithery / mcp.so / Glama listings:
Name:
ezstat-mcp-serverDisplay name: EzStat
Description: Push and read metrics with EzStat from any AI agent. Track counters and values, then ask natural-language questions over your production telemetry.
Homepage: https://ezstat.dev
Version:
0.8.0Transport:
stdio(local) +streamable-http(hosted athttps://mcp.ezstat.dev)License: MIT
Repository: this package's source
The mcp block in package.json carries the same metadata so registry crawlers pick it up.
Listing steps (what to submit, where)
Smithery: push the repo to GitHub, then run
npx -y @smithery/cli publish(or submit via the web UI). The shippedsmithery.yamlprovides the install schema.mcp.so: submit at https://mcp.so/submit with the metadata above + the GitHub URL.
Glama: submit at https://glama.ai/mcp/submit.
The package is not published to npm automatically — publish manually after a release review.
License
MIT.
Available Tools
4 toolsask_ezstatA
Ask a natural-language question about the account's metrics. This is the agent-read path: send a question like "what spiked yesterday?" or "compare signups vs cancellations last 7 days" and EzStat returns a grounded answer plus the underlying data it used.
Inputs: query (required).
list_statsA
List the account's metrics — names and types. Call this first when you don't know what stats exist in the account.
Inputs: type.
read_statA
Read a single stat: latest value, recent series, and basic summary (count/min/max/avg/sum). Use this for a structured read of one metric — prefer ask_ezstat for free-form questions over many stats.
Inputs: name (required), to, from, resolution.
track_metricA
Record a metric point your app "produced". Use this whenever the surrounding code emits a counter or gauge — e.g. on a page view, a successful checkout, or a measured latency. Counters are +N (default +1), values are gauges (e.g. 42.5 ms).
Inputs: stat (required), count, value, timestamp.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
Each tool has a clear, distinct role: track_metric writes, read_stat does structured reads of a single metric, ask_ezstat handles free-form natural-language queries, and list_stats discovers available metrics. The overlap between read_stat and ask_ezstat is explicitly addressed in their descriptions, making misselection unlikely.
Most tools follow a clear verb_noun pattern: track_metric, read_stat, list_stats. ask_ezstat also follows verb_noun but uses a product-specific noun, which is slightly inconsistent with the generic metric-related nouns used elsewhere. Overall the pattern is still predictable and readable.
Four tools is on the smaller side but appropriate for the focused scope of tracking and querying metrics. Each tool serves a core need without redundancy, though the set could be expanded with e.g. delete_metric or batch operation tools.
The tool surface covers the essential lifecycle: record (track_metric), read single (read_stat), query over many (ask_ezstat), and discover (list_stats). It lacks explicit update/delete operations, but for a metrics tracking server these are often unnecessary, and agents can work around this limitation.
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