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TensorLink-AI

Ephemeris MCP server

Official

Get credit balance

get_balance
Read-onlyIdempotent

Check the account's spendable credit balance in millicredits, excluding credits reserved by in-progress forecasts; 1,000 millicredits equal one credit.

Instructions

Return the account's spendable credit balance in millicredits, excluding credits reserved by in-progress forecasts. 1000 millicredits equal one credit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
balance_mcNoSpendable millicredits (ledger minus active holds).
active_holds_mcNoMillicredits reserved by in-flight forecasts.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare the operation read-only, idempotent, closed-world and non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: the balance excludes credits reserved by in-progress forecasts, and the millicredit conversion is spelled out. It does not mention caching or refresh semantics, which keeps it short of a 5.

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

Conciseness5/5

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

Two tight sentences with zero waste; the resource and its qualifying scope come first, and the unit conversion follows. Nothing is repeated from annotations or schema.

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 a full output schema present, the description need not explain return values, and the annotations carry the safety profile. The remaining ambiguity a caller would care about – what 'spendable' excludes and the unit – is explicitly resolved.

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 takes no parameters, so there is nothing for the description to document; the baseline for a zero-parameter tool is 4. The millicredit definition is a useful unit clarification rather than a parameter explanation.

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?

States a specific verb and resource ('Return the account's spendable credit balance') and pins down scope with 'spendable' and the millicredit unit. This cleanly separates it from siblings like get_usage and forecast, which report consumption or projections rather than current available funds.

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

Usage Guidelines3/5

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

Usage is implied by the name and the 'spendable balance' framing, but there is no explicit statement of when to call this versus get_usage or forecast. An agent can infer it, yet the definition never routes the caller or names exclusions.

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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