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recommend_route

Suggests a suitable AI provider for a task category by applying editable fit heuristics, weighing capabilities, context, past outcomes, and explicit user requests.

Instructions

Suggest a provider using editable task-fit heuristics. The coordinator chooses based on actual capabilities, context, previous outcomes and explicit user requests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoOptional task text for relevant installed specialist skill hints and video routing
categoryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It usefully discloses that the output is a suggestion, that the heuristics are editable, and that the coordinator makes the final call based on capabilities and prior outcomes — important advisory semantics. It says nothing about cost, latency, determinism, or whether the recommendation is binding, so coverage is partial.

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?

Two tight sentences with no filler, and the core purpose is front-loaded in the first. The second sentence is slightly abstract but does add the decision rationale.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter advisory tool with no annotations and no output schema, the description should clarify what comes back (a single provider? a ranked list?) and whether the result is consumed automatically. It leaves the return contract unstated, which is a meaningful gap even for a low-risk recommendation call.

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

Parameters2/5

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

Schema description coverage is 50% — 'task' is documented but 'category' is only an enum with no field description. The description adds no parameter-level meaning, no hints on how category interacts with task, and no explanation of the enum values beyond what the schema shows. It does not compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Suggest a provider using editable task-fit heuristics.' An agent can tell this is an advisory routing tool, distinct from the sibling actions (submit_task, get_task, cancel_task). It stops short of explicitly naming how it differs from those siblings, so it is clear rather than sharply differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the basis of the selection ('actual capabilities, context, previous outcomes and explicit user requests') but never states when to call this instead of submit_task or list_workers. Usage is only implied — presumably before submitting work — leaving the agent to infer the workflow position.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.