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adefemi-dev
by adefemi-dev

route

Map a natural-language task to the appropriate catalog actions with ranked recommendations and argument templates. Optionally execute the top recommendation to return the result in the same call.

Instructions

Map a natural-language task to the best catalog action(s): route(task, repo?, execute?). Returns ranked recommendations with ready-to-run argument templates. With execute=true, dispatches the top recommendation and returns its result in the same call, collapsing discover-then-call into one round-trip. Recommends assemble_task_context / plan_turn for context-gathering intents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoNoRepository identifier (required to execute repo-scoped actions).
taskYesWhat you're trying to do, in plain language.
modelNoOptional active model id; piggybacks tier-switch like plan_turn(model=...).
executeNoIf true, dispatch the top recommended action and return its result.
Behavior4/5

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

The annotations provide readOnlyHint=false and openWorldHint=true, so the tool is not read-only. The description builds on this by disclosing that execute=true dispatches the top recommendation and returns the result, collapsing the discover-then-call flow into one round-trip. It also clarifies that without execute it returns recommendations only, giving the agent a clear behavioral model without contradicting annotations.

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 three sentences, front-loaded with the core purpose, then explains the execution option and a usage hint for context-gathering intents. Every sentence earns its place; no filler or repetition of schema details.

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?

For a router that can optionally execute, the description covers the main behavior (recommendations vs. execution), the return type (ranked recommendations with argument templates), and even suggests related tools for specific intents. It does not detail error handling or authentication, but these are not essential given the tool's simplicity and the presence of a schema with parameter semantics.

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

Parameters3/5

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

The input schema contains full descriptions for all 4 parameters (100% coverage), including task, repo, model, and execute. The description does not add new parameter-specific semantics; it reiterates the execute behavior already documented. Given the schema covers meaning, the description adds no v、alue here, aligning with the baseline of 3.

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 explicitly states 'Map a natural-language task to the best catalog action(s)' – a specific verb + resource that clearly distinguishes it from sibling tools like set_tool_tier or menu, which are direct actions. It also describes its primary output (ranked recommendations) and the optional execution path, making its purpose unambiguous.

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 clarifies when to use the tool by indicating it is for natural-language task routing and explicitly recommends assemble_task_context / plan_turn for context-gathering intents. However, it does not explicitly state exclusions (e.g., 'use when you know the exact action'), though this is strongly implied by the router nature. It provides actionable context without being prescriptive about alternatives.

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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