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Moatlight

by com.moatlight
OverviewDiscussionsAdmin

The signed-in user, plan and limits

get_account
Read-only

Returns the user's plan (free or early_access), when the pass ends, what the plan allows, and how to upgrade. Needs login with scope account:read.

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_account"
  }
}

Input Schema

NameRequiredDescriptionDefault
fieldsNoComma-separated top-level fields to return, for example fields=id,stage. Omit to get everything. Spend fewer tokens by asking for less.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / fields
      Added value: +{
+  "description": "Comma-separated top-level fields to return, for example fields=id,stage. Omit to get everything. Spend fewer tokens by asking for less.",
+  "maxLength": 200,
+  "type": "string"
+}
  2. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, non-destructive, closed-world behavior, and the description adds the important behavioral fact the annotations cannot express: the required auth scope account:read. It also enumerates the returned content (plan, pass expiry, limits, upgrade instructions), which is valuable since no output schema exists.

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?

One compact sentence that front-loads the return content and ends with the auth prerequisite. No filler or repetition; only slight cost is that the auth clause competes with the purpose clause for the most prominent position.

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 single-optional-parameter read tool with no output schema, the description covers what is returned, what the values mean (free vs early_access), and the access requirement. It is largely self-sufficient, with the only gap being how it relates to pricing/product 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?

There is a single optional 'fields' parameter with 100% schema coverage, so the schema already explains the comma-separated projection and token-saving rationale. The description adds nothing about it, so the baseline of 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?

States a specific verb ('Returns') plus concrete resources: the user's plan type (free or early_access), expiry date, entitlements, and upgrade path. An agent can tell what it will get. It does not, however, distinguish itself from siblings such as get_pricing or get_product, which could plausibly answer overlapping pricing questions.

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?

The prerequisite 'Needs login with scope account:read' is stated, which is real usage guidance. There is no explicit when-to-use/when-not guidance and no routing away from get_pricing or get_product for adjacent questions, so selection remains partly inferential.

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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