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Metis — Record Decision

record_decision

Record user preferences and decisions to personalize future interactions and avoid asking the same questions repeatedly.

Instructions

Record a user preference or decision so Metis adapts to the user over time.

Use this whenever the user states (or confirms) a standing preference or makes a
decision worth remembering — coding style, citation format, a methods default, an
article/dataset they keep returning to, a naming convention, a workflow choice.
These are recalled into context on future requests (recall_decisions), so Metis
personalizes instead of asking again.

Args:
    decision: the preference/decision in plain language (e.g. "Always use tidyverse style in R, never base apply").
    category: preference | coding | citation | methodology | writing | article-ref | dataset | routing | other.
    context: optional — when/why it applies (e.g. "for HAT spatial analyses").
    scope: 'always' (persist) or 'once'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoalways
contextNo
categoryNopreference
decisionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Describes that decisions are 'recalled into context on future requests', indicating persistence. The scope parameter ('always' vs 'once') is explained in param semantics, adding behavioral detail. No annotations are provided, so the description carries the burden; it covers persistence and adaptability well.

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 concise and well-structured: a clear opening sentence, usage guidelines, and a parameter list. It could trim slightly but remains efficient and front-loaded with purpose.

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 low complexity, the description fully covers purpose, usage, parameters, and behavioral implications. The existence of an output schema reduces the need to detail return values. No gaps remain for effective agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description provides thorough explanations for all four parameters: decision (plain language example), category (enumerated values), context (optional applicability), and scope (persist vs once). This adds critical meaning beyond the schema structure.

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 verb 'Record' and the resource 'user preference or decision', with a purpose ('so Metis adapts to the user over time'). It distinguishes itself from sibling tools like 'recall_decisions' and 'add_memory_entry' by focusing on decisions/preferences and referencing how they are recalled.

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?

Provides explicit guidance on when to use ('whenever the user states a standing preference or makes a decision worth remembering') with concrete examples. Does not explicitly state when not to use or list alternatives, but the context and sibling list imply differentiation. Lacks full exclusionary guidance.

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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