Galatic.ai

Account status

get_account_status

Read-onlyIdempotent

Get the signed-in account in one call: { me, usage }. me.user has username, email, name and sub (no plan). usage has tier, tokensUsed, tokenBudget, tokensRemaining, topUpTokens, per-type counts, the month key, plan limits, storage and running jobs, and the full table of credit costs per model. Use it before an expensive job to check the balance; use get_profile for the public profile and get_model_pricing for prices. Spends no credits; the first call for a new account also creates its profile.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "get_account_status"
  }
}

Input Schema

Name	Required	Description
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 24, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds real extra context: 'Spends no credits' and the non-obvious side effect that the first call for a new account creates its profile. That lazy-initialization disclosure goes beyond the structured data. It stops short of 5 only because it never clarifies rate limits or that the profile-creation is the one mutation hidden behind a readOnly hint.

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?

Front-loaded with the response shape, then routing and side effects, with zero filler sentences. The field enumeration is long but each item earns its place by pre-signaling the return structure. Slightly dense but well organized.

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?

There is no output schema, yet the description fully enumerates the returned fields (tier, token budgets, plan limits, storage, credit costs, etc.), so an agent knows what it receives. Combined with the side-effect note and sibling routing, nothing essential is missing.

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?

Schema coverage is 100%, but the three parameters (context, llm_model, conversation_id) are analytics plumbing rather than tool-specific inputs, and the description says nothing about them. Baseline 3 applies since the schema fully documents these fields and the description needn't restate them.

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 ('Get the signed-in account') and enumerates exactly what the response contains ({ me, usage }) down to individual fields. It explicitly distinguishes itself from get_profile ('no plan') and get_model_pricing, so an agent can tell the three apart without opening a schema.

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

Usage Guidelines5/5

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

Gives an explicit trigger ('Use it before an expensive job to check the balance') and names the two alternatives with the conditions that select them ('get_profile for the public profile and get_model_pricing for prices'). Nothing is left 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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