Ephemeris Time-Series Forecasting

List forecasting models

list_models

Read-onlyIdempotent

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.

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`models`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 19, 2026

changedOutput schema / properties / models / items / properties / billing_context_cap / description

Previous value: -"Most context points billed per variate; longer inputs are billed at the cap."New value: +"Legacy compatibility field, now null. History length does not affect billing; model input limits still apply."

addedOutput schema / properties / models / items / properties / billing_horizon_steps
```
Added value: +{
+  "const": 64,
+  "type": "number"
+}
```

addedOutput schema / properties / models / items / properties / billing_unit

Added value: +{
+  "const": "millicredits_per_series_slot_per_horizon_block",
+  "type": "string"
+}

changedOutput schema / properties / models / items / properties / price_per_kslot_mc / description

Previous value: -"Millicredits per thousand series-slots; null when the model is not billable."New value: +"Millicredits per target series-slot per 64 forecast steps; no history multiplier. Null when unpriced."

First observedOct 7, 2026

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety behavior is covered. The description adds useful context beyond the annotations: the panel reflects current health, includes pricing units and a billing multiplier nuance, and can differ from documentation. It does not contradict the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and every clause adds relevant information (capabilities, pricing, usage caveat). However, the single dense sentence is harder to scan than a bulleted list, slightly reducing readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and an existing output schema, the description need not explain return values in detail, yet it covers what is listed and when to call the tool. It is complete enough for correct invocation, with only a minor gap in not explicitly routing to describe_forecast for single-model details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there is nothing for the description to clarify beyond the empty schema. The baseline for zero parameters is 4; the description appropriately focuses on output content rather than parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Discover') and resource ('the model panel') and enumerates the model attributes returned (availability, capabilities, ensemble weights, price). It does not explicitly differentiate itself from the sibling describe_forecast tool, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear usage context: 'Call this before using forecast in explicit mode' and warns that the deployed panel can differ from documentation. It does not state when NOT to use it or name describe_forecast as an alternative, leaving some guidance implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Ephemeris Time-Series Forecasting tools Inspect

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