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SVerITG

Metis

by SVerITG
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Metis — Get User Profile

get_user_profile

Retrieve the user's identity, interests, news topics, communication style, and default model to tailor responses.

Instructions

Return the user's identity, interests, style, and model preference.

Call this at the start of any personalised run to understand the user's
topics, news signals, and communication preferences.

Returns JSON with:
- display_name: user's display name (set via /metis_config)
- role: professional role (e.g. "Senior researcher")
- interests: list of research interest tags
- news_topics: list of news monitoring topics
- active_model: current default model slug (haiku / sonnet / opus)
- style: dict of communication preferences:
    - response_length: "concise" | "moderate" | "detailed"
    - feedback_style: "gentle" | "direct" | "challenging"
    - challenge_level: "supportive" | "balanced" | "rigorous"
    - warmth: "warm" | "neutral" | "formal" (default: "warm")
    - detail_level: "brief" | "balanced" | "thorough" (default: "balanced")
    - routing_verbosity: "silent" | "natural" | "detailed" (default: "natural")

Usage pattern:
  profile = json.loads((await get_user_profile())[0].text)
  interests = profile['interests']
  style = profile['style']  # → {"response_length": "concise", "warmth": "warm", ...}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

No annotations provided, so description must cover behavior. It details the return structure (fields, nested style dict) and implies read-only nature. It doesn't mention side effects or authorization, but for a simple profile lookup, this is adequate.

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?

The description is concise, well-structured, and front-loaded. First sentence states purpose, then usage guidance, detailed output fields, and a code example. No unnecessary words.

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?

Given no parameters and an existing output schema, the description provides complete context: when to use, detailed output format, and a usage pattern. The code example further aids correct invocation.

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?

No parameters exist (input schema empty), so schema description coverage is 100%. Baseline for 0 params is 4. The description adds value by explaining output fields, though not param-related.

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?

The description clearly states 'Return the user's identity, interests, style, and model preference.' It specifies the resource (user profile) and action (return). Among siblings, it uniquely focuses on personal profile data.

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

Usage Guidelines4/5

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

Explicitly says 'Call this at the start of any personalised run,' providing clear context for when to invoke. While it doesn't mention when not to use or list alternatives, the guidance is sufficient for typical use.

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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