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nucleus

by io.github.eidetic-works
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Cost Router

nucleus_route
Read-only

W5 tier routing — route a prompt to the optimal model tier.

Actions: route - Route a prompt to cheapest capable model. params: {prompt, complexity?, context?, estimated_output_tokens?} complexity ∈ 'routine' | 'complex' | 'sovereign' (default 'routine') Returns: provider, model, cost estimates, sovereignty tier.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "action": "<action>"
    },
    "name": "nucleus_route"
  }
}

Input Schema

NameRequiredDescriptionDefault
actionYes
paramsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnly and non-destructive behavior. The description adds transparency by detailing the routing logic (complexity tiers, cost estimation) and the return structure (provider, model, cost, sovereignty tier). No contradictions with 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 concise, using a clear heading and bulleted list to describe the action and its parameters. Every sentence provides necessary information without redundancy, and the structure is easily scannable.

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?

The description covers the tool's core purpose, parameters, and return values adequately for its complexity. The presence of an output schema reduces the need for detailed return descriptions. However, it lacks information on error handling or preconditions, which are minor 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?

The input schema has 0% description coverage and only defines action and params as generic objects. The description compensates by detailing expected parameters (prompt, complexity with enum, context, estimated_output_tokens) and their meanings, providing essential clarity beyond the schema.

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 the tool routes a prompt to the optimal model tier, specifying it selects the cheapest capable model. This clearly distinguishes it from sibling tools like nucleus_agents or nucleus_delegate, which handle different concerns.

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?

The description explains that the tool is for routing prompts to cost-optimal models, but it does not provide explicit guidance on when to use it versus alternatives, nor does it mention prerequisites or exclusions. Usage is implied but not fully contextualized.

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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