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get_user_profile

Retrieve the user's identity, interests, and active model preference to enable personalized interactions and priority news signals.

Instructions

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

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

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 (e.g. ["your research area", "a method you use"])
- news_topics: list of news monitoring topics (e.g. ["a topic you follow", "AI in your field"])
- active_model: current default model slug (haiku / sonnet / opus)

Usage pattern:
  profile = json.loads((await get_user_profile())[0].text)
  interests = profile['interests']   # → ["your research area", "a method you use"]
  news_topics = profile['news_topics']  # → ["a topic you follow", "AI in your field"]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. It fully discloses the return format (JSON with fields like display_name, role, interests, etc.) and gives example usage, making behavior transparent. No side effects are mentioned, but it's a read-only tool.

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?

The description is well-structured with sections for purpose, usage, return format, and example. It is slightly verbose but clear and front-loaded with the most important information.

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 the tool has no parameters and an output schema exists, the description covers the purpose, when to use, and the return fields in detail, making it complete for an agent to understand and invoke correctly.

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 input schema has zero parameters, so baseline is 4 per rubric. The description adds value by explaining the return structure beyond the schema, which is sufficient.

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 the tool returns the user's identity, interests, and active model preference, using specific verbs and resource. It distinguishes itself from siblings by being about the user's profile, not other get_ tools.

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?

The description explicitly says 'Call this at the start of any personalised run' to understand the user's topics and news signals, providing clear context for use. It does not mention when not to use or alternatives, but the guidance is adequate.

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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