Ephemeris Time-Series Forecasting

industries.cascade/ephemerisVersion 1.0.1

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

Status

Responds306 msNov 14, 2026, 14:32 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:32 UTC
Latency: 306 ms
Remotes responding: 1 of 1
Sign-in: API key
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 22:14 UTC

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Connect

Streamable HTTP

https://ephemeris.cascade.industries/api/mcpResponds306 msNov 14, 2026, 14:32 UTC

No sign-in

Claude Code

claude mcp add --transport http ephemeris https://ephemeris.cascade.industries/api/mcp --header 'Authorization: Bearer <YOUR_TOKEN>'

Replace <YOUR_TOKEN> before running.

Claude Desktop and claude.ai

Settings > Connectors > Add custom connector
Name: ephemeris
Remote MCP server URL: https://ephemeris.cascade.industries/api/mcp
This server expects the header Authorization.

Cursor

{
  "mcpServers": {
    "ephemeris": {
      "url": "https://ephemeris.cascade.industries/api/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

Replace <YOUR_TOKEN> before running.

VS Code

code --add-mcp '{"name":"ephemeris","type":"http","url":"https://ephemeris.cascade.industries/api/mcp","headers":{"Authorization":"Bearer <YOUR_TOKEN>"}}'

Replace <YOUR_TOKEN> before running.

Streamable HTTP

npm: ephemeris-mcp 1.0.0

Package only

npm (npx)

claude mcp add ephemeris -e 'EPHEMERIS_API_KEY=<YOUR_EPHEMERIS_API_KEY>' -- npx -y ephemeris-mcp@1.0.0

Replace <YOUR_EPHEMERIS_API_KEY> before running.

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Tools

7 tools

Tools of Ephemeris Time-Series Forecasting
	Description		Read-only	Idempotent	Open world
`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.	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.	No	Yes	Yes	No
`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.	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.	No	No	No	Yes
`get_balance`Return the account's spendable credit balance in millicredits, excluding credits reserved by in-progress forecasts. 1000 millicredits equal one credit.	Return the account's spendable credit balance in millicredits, excluding credits reserved by in-progress forecasts. 1000 millicredits equal one credit.	No	Yes	Yes	No
`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.	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.	No	Yes	Yes	No
`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.	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.	No	Yes	Yes	No
`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.	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.	No	Yes	Yes	No
`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.	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.	No	Yes	Yes	No

Details

Registry status: Active
Published: Oct 6, 2026
Updated: Oct 6, 2026
Website: https://ephemeris.cascade.industries/⁠ (opens in a new tab)
Repository: https://github.com/TensorLink-AI/ephemeris-mcp⁠ (opens in a new tab)
Categories (suggested): Data and analyticsSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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