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Ephemeris MCP server

Ephemeris MCP server

Official
by TensorLink-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Get recent usage

get_usage
Read-onlyIdempotent

Retrieve your account's forecast request log, newest first, to review mode, models, series count, horizon, credits, HTTP status, and latency for each request.

Instructions

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.

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": "get_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoRows per page, 1 to 200. Defaults to 50.
offsetNoRows to skip. Use `pagination.next_offset` from the previous page.

Output Schema

NameRequiredDescriptionDefault
dataNoRequest log rows, newest first.
limitNo
offsetNo
paginationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds useful context by stating the log is returned newest first and listing settled/estimated credits plus HTTP status and latency, but it does not contradict the annotations. With annotations carrying the safety profile and an output schema present, this is adequate but not rich.

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 a single front-loaded sentence that states the resource first and then enumerates returned fields efficiently. The field list is long but every element adds informative value, with no filler.

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?

For a simple read-only listing tool with rich annotations, full schema coverage, and an output schema, the description supplies what is needed to understand the return ordering and content. The primary missing piece is guidance on when to select this tool over its siblings.

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

Parameters3/5

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

The input schema fully documents both parameters (limit and offset) with ranges and pagination guidance, giving 100% schema coverage. The description adds no parameter information, which is acceptable because the schema does the heavy lifting; baseline 3 applies.

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 uses a specific verb and resource ('Return the account's forecast request log') and specifies ordering ('newest first'). It does not explicitly distinguish this tool from siblings like forecast or get_balance, so it is clear but lacks sibling differentiation.

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

Usage Guidelines2/5

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

The description gives no indication of when to use this tool versus alternatives such as forecast, get_balance, or list_models. It also omits any prerequisites or exclusions, leaving usage entirely to inference.

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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