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Metis — Record Routing Preference

record_routing_preference

Record the user's routing preference for requests: set a persistent rule or acknowledge a one-time route to a specific agent.

Instructions

Active-learning routing. After Metis asks the user 'should I always route requests like this to , or just this once?', call this with their answer.

scope='always' writes a permanent, high-priority rule (beats the built-in seeds)
into the routing DB — part of institutional memory, persists across Metis updates.
scope='once' is acknowledged but not stored.

Args:
    phrase: the trigger word/phrase the user wants routed (e.g. "spatial scan").
    agent_slug: which agent to route it to (e.g. "epidemiologist").
    scope: 'always' (persist) or 'once' (don't store).
    priority: lower = more specific / checked first (default 5 beats all seeds).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoalways
phraseYes
priorityNo
agent_slugYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description effectively discloses key behaviors: scope='always' persists permanently with high priority, scope='once' is acknowledged but not stored, and the priority default beats seeds. It does not mention what happens with conflicting rules, but is transparent enough for the tool's simplicity.

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 very concise, around 100 words, with a clear introductory sentence followed by a structured Args list. Every sentence contributes meaningful information, no fluff.

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?

Given the tool's simplicity and the presence of an output schema (not shown but noted), the description provides sufficient context. It covers the core behavior and parameters, though some edge cases (e.g., duplicate rules) are left unaddressed.

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?

Schema coverage is 0%, so the description must add meaning. It explains each parameter: phrase (trigger word), agent_slug (target agent), scope ('always' vs 'once'), and priority (lower = more specific, default 5 beats seeds). This adds significant value beyond the schema's type/default information.

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's purpose: recording the user's answer to Metis' routing preference question. It specifies the verb ('call this with their answer') and the resource (the routing preference). This distinguishes it from sibling tools, which are mostly unrelated.

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 states when to use the tool: after Metis asks the routing question. While it doesn't list alternatives or when not to use it, the context is very specific and leaves little ambiguity.

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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