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Ephemeris MCP server

Ephemeris MCP server

Official
by TensorLink-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

List forecasting models

list_models
Read-onlyIdempotent

Discover the current model panel: each model's availability, capabilities, ensemble weights, and pricing. Call before explicit forecasting to verify the deployed models.

Instructions

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 per thousand series-slots in millicredits and maximum billable context. Call this before using forecast in explicit mode; the deployed panel can differ from any documentation.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
modelsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context about dynamic availability ('right now (healthy)') and documentation divergence, but does not mention permissions, rate limits, or pagination.

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?

It is front-loaded with the core purpose and efficiently enumerates the rich output in a single sentence, followed by a direct usage note. It is dense but every clause appears to earn its place.

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

Completeness5/5

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

With an output schema present and annotations covering safety, the description supplies the remaining context an agent needs: what the tool reveals, why it matters before forecasting, and that the panel is dynamic. Nothing essential is missing.

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 parameter semantics are inherently trivial. The description lists returned fields rather than input parameters, which is appropriate but adds no parameter-level guidance.

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

Purpose5/5

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

The description states a specific verb+resource ('Discover the model panel') and enumerates exactly what is returned: availability, capabilities, ensemble weight, revision, price, and billable context. It also distinguishes the tool from sibling 'forecast' by positioning it as a prerequisite for explicit mode.

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 explicit when-to-use guidance: 'Call this before using forecast in explicit mode' and explains why: 'the deployed panel can differ from any documentation.' It does not discuss when not to use it, but no relevant alternative is omitted.

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

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