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get_user_profile

Retrieve the user's profile including research interests, news monitoring topics, and active model to personalize responses.

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 exist, so description carries full burden. It discloses the return format (JSON with specific fields) and implies no side effects. Lacks mention of potential errors or authorization, but for a simple read it is sufficient.

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?

Extremely concise: one sentence for purpose, one for when to use, then a structured list of return fields, and a compact usage example. Every sentence adds value.

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's simplicity (0 params, output schema exists), the description fully covers what an agent needs: purpose, invocation timing, return structure, and parsing pattern. No gaps.

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; schema coverage is 100% (empty schema). Baseline score of 4 is appropriate since description adds no param info, but none is needed.

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 user identity, interests, and active model preference. It distinguishes itself from siblings by being a dedicated profile getter, and provides explicit field names.

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?

Directly advises to call at the start of any personalised run, which is a clear usage scenario. Also includes a code pattern showing how to parse the result, aiding proper invocation.

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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