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Ephemeris Time-Series Forecasting MCP Server

not responding

Ephemeris Time-Series Forecasting is listed as active in the registry but did not answer our last check. 55 installs a week from npm. It exposes 7 tools. Last commit 23 Oct 2026.

Probabilistic time-series forecasts from zero-shot foundation models: routed, single or ensembled.

Installs per day peak 262 · avg 8
a month agotoday
Uptime history 34 days of history · worst day 0%
34 days agonow
0.0%
Uptime 24h
0 of 91 checks
7
Tools
read from the server
349 ms
Response time
average over 24h
55
Installs / week
npm and PyPI

What changed 9

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 8 October 2026. No other catalogue keeps this.

25 Oct 2 tool descriptions were rewritten get_version, list_models
25 Oct a tool changed the parameters it asks for forecast
18 Oct 3 tools appeared describe_forecast, get_version, plot_forecast
18 Oct a tool description was rewritten forecast
18 Oct a tool changed the parameters it asks for forecast
8 Oct a tool description was rewritten forecast

What the code does

We read the source, 3 h ago · tools taken from the live server · rules 995398eba69f

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

    const child = spawn(cmd, args, { stdio: "ignore", detached: true });

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 11 min ago.

run in your terminal
claude mcp add ephemeris --transport http https://ephemeris.cascade.industries/api/mcp
Settings → Connectors → Add custom connector, then paste this address
https://ephemeris.cascade.industries/api/mcp
~/.codex/config.toml
[mcp_servers.ephemeris]
url = "https://ephemeris.cascade.industries/api/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "ephemeris": {
      "url": "https://ephemeris.cascade.industries/api/mcp"
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "ephemeris": {
      "type": "http",
      "url": "https://ephemeris.cascade.industries/api/mcp"
    }
  }
}

Available tools 7

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

balance
get_balance
Return the account's spendable credit balance in millicredits, excluding credits reserved by in-progress forecasts. 1000 millicredits equal one credit.
describe
describe_forecast
Deterministic calculations from a complete ephemeris.forecast v1 artifact object, including CLI artifacts. No inference or forecast charge; this hosted tool still receives data over the network. Returns scope, result, basis, limitations and source pointers: last-observed baseline, median endpoint/change, marginal-median aggregates, and exact saved billing units. Missing medians/context remain unknown. Sum of medians only for period_total; it is not a cumulative-distribution interval or median. No calibrated coverage, causal explanation, path/peak probability, or business action is inferred. Example: pass {artifact: <forecast result artifact>}; choose series_index/variate_index for multiple results. Maximum 1 MiB; malformed/version/shape errors are recoverable by fixing the artifact or using CLI, never rerunning inference. Missing labels may be corrected in a COPY of context without changing request/response. No filesystem access, URL fetches, persistent storage, Gnomon import, or scoring.
forecast
forecast
Submit numeric observations to forecast future sampling steps. Spends credits on successful calls. Choose and retain idempotency_key before submitting; no automatic retries, replay depends on server retention. Minimal example: {mode: route, series: [{values: [10,12,11], freq: D}], horizon: 2, quantiles: [0.1,0.5,0.9]}. Returns forecasts and meta as before, plus artifact by default for describe_forecast/plot_forecast. Save the embedded JSON attachment: it includes full input history and local context; no hosted storage is created. Context never becomes a model input. Billing is at meta.billing in millicredit strings (100 mc = 0.1 credits). Missing future inputs mean not supplied, not no events. Quantile bands do not establish coverage, cumulative intervals or peak probabilities. After uncertain completion, reuse the same key and API request; an expired key may cause a new charge.
models
list_models
Discover the model panel: each model's availability right now (healthy), capabilities (multivariate, covariates, max_horizon, auto_max_horizon), its weight in an ensemble at each horizon, pinned and actually served weight revision, price in millicredits per series-slot per 64 forecast steps. History length has no billing multiplier. Call this before using forecast in explicit mode; the deployed panel can differ from any documentation.
plot
plot_forecast
Render a complete ephemeris.forecast v1 artifact object as PNG (default inline preview) or SVG (embedded resource to save). No inference or forecast charge; hosted processing receives the artifact over the network. Example: {artifact: <saved artifact>, format: png}. Requires returned q0.5; never invents a median. Observed history, forecast boundary, median and nominal quantile bands are labeled. Default history view is most recent max(24, 2*horizon) points, capped by available history; history: all shows all. Dates only when supported by recorded metadata, otherwise sampling steps. Bands do not establish calibrated coverage; missing/crossed points are omitted and counted. Maximum 1 MiB input. Select series_index/variate_index for multiple results. Fix argument/shape errors without rerunning inference. User-supplied labels can be corrected in a COPY of artifact context offline; no new forecast needed. Client decides preview/download UI. URNs identify attached bytes, not hosted URLs; no filesystem access, URL fetching or persistent storage.
usage
get_usage
Return the account's forecast request log, newest first, with the mode, models used, series count, horizon, credits estimated and settled, HTTP status and latency of each request.
version
get_version
Read this deployed MCP server's version, nullable build commit and supported saved forecast schema versions. Call with {}. No API key, account access, inference, charges or writes; network access to this server only. Returns component, version, build_commit (full source SHA or null when unavailable), artifact_schema_versions, and runtime_tls (Node release and certificate-trust status; metadata only, no probe connections). Unknown commit stays null. Server, CLI, npm bridge and artifact schema versions are independent; this tool cannot inspect local installations, check the latest registry release or establish model quality. Use list_models for live model capabilities. After a connection failure reconnect and retry; no forecast could have been submitted by this tool.

Endpoints

URLTransportStateLatencyChecked
https://ephemeris.cascade.industries/api/mcp streamable-http answering 349 ms 11 min ago

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Ephemeris Time-Series Forecasting — questions

Answers built from our own checks of this server.

What can Ephemeris Time-Series Forecasting do?
It exposes 7 tools, read directly from the server on our last check. Among them: describe_forecast, forecast, get_balance, get_usage, get_version, list_models and 1 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Ephemeris Time-Series Forecasting working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 0 of 91 checks got a reply (0.0%), average response time 349 ms. The bar chart above shows every period we have measured.
The registry lists Ephemeris Time-Series Forecasting as active — why does it not respond?
The official MCP registry stores what the author submitted; it does not verify that the server still runs. We check the endpoint ourselves, and this one does not answer. Catalogues that copy the registry without checking will show it as working.
How do I connect Ephemeris Time-Series Forecasting?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Ephemeris Time-Series Forecasting need an API key?
No. Ephemeris Time-Series Forecasting completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 7 of them are readable on this page. This is what we observed, not what the docs claim.
How many people use Ephemeris Time-Series Forecasting?
The npm package ephemeris-mcp was installed 55 times in the last week. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is Ephemeris Time-Series Forecasting open source?
The code is open source: it is published under the MIT licence, written in JavaScript and 0 stars on GitHub. The licence covers the code in the repository, not the hosted service at ephemeris.cascade.industries, which runs on its operator's own terms.