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by ai.galatic
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Get profile

get_profile
Idempotent

Get the signed-in user's creator profile as { profile }: displayName, bio, website, location, socialLinks, avatarUrl, coverUrl, isPublic, featured and hidden items, timestamps. The first call creates a default profile if the user has none. Use update_profile to change it, and get_account_status for plan and credit usage.

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_profile"
  }
}

Input Schema

NameRequiredDescriptionDefault
contextYesExplain 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_modelYesThe 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_idNoPass 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.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The key disclosure — "The first call creates a default profile if the user has none" — explains why the annotations set readOnlyHint=false despite the name being a getter, and warns that the call is not purely passive. It also clarifies the implied scope (signed-in user only, not arbitrary users). It stops short of describing response shape quirks or failure modes, but the annotation profile already covers safety and idempotency.

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?

Three sentences: what it returns, the important side effect, and the alternatives — front-loaded with no filler. The field enumeration is dense but functional rather than decorative.

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?

With no output schema, the description usefully enumerates the returned profile fields and flags the first-call creation behavior. The phrasing "featured and hidden items" is slightly vague about structure, but nothing critical for invoking the tool 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 description coverage is 100%, so context, llm_model, and conversation_id are fully documented in the schema. The description adds nothing about how to supply them, which is acceptable given that they are analytics/conversation plumbing rather than tool-specific inputs.

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?

Uses a specific verb and resource ("Get the signed-in user's creator profile") and then enumerates the returned fields, so an agent knows exactly what it is fetching. It explicitly names the two nearest siblings, update_profile and get_account_status, and explains what they cover instead.

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?

The description states when to use this tool (current signed-in user's profile), names the alternative for mutation (update_profile) and the alternative for plan/credit data (get_account_status). That is a clear use/use-something-else split with no ambiguity about read vs. write.

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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