lazymac-mcp

llm_router

Automatically selects the most cost-effective large language model (GPT-4, Claude, or Haiku) that meets specified quality requirements for AI tasks.

Instructions

Auto-pick cheapest LLM (GPT-4 / Claude / Haiku) that meets your quality bar

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "llm_router"
  }
}

Input Schema

Name	Required	Description	Default
`params`	No	Free-form params object — passed as query string for GET, JSON body for POST

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Apr 15, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'auto-pick' behavior and quality/price criteria, but doesn't disclose important behavioral traits: what happens when no LLM meets the quality bar, whether it makes actual API calls or just recommends, what authentication is needed, rate limits, error handling, or response format. For a routing tool with no annotation coverage, this is insufficient.

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 a single, efficient sentence that immediately conveys the core functionality. Every word earns its place: 'Auto-pick' (action), 'cheapest LLM' (criteria), '(GPT-4 / Claude / Haiku)' (examples), 'that meets your quality bar' (constraint). No wasted words or unnecessary elaboration.

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

Completeness2/5

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

Given the complexity of an LLM routing tool with no annotations and no output schema, the description is incomplete. It doesn't explain how quality is determined, what the output looks like, error conditions, or integration details. For a tool that presumably makes routing decisions between multiple AI providers, more context about behavior and results is needed.

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 has 100% description coverage, documenting a single 'params' object that accepts free-form parameters. The description adds no parameter-specific information beyond what's in the schema. Since schema coverage is high (>80%), the baseline score of 3 is appropriate - the description doesn't compensate but doesn't need to given the schema documentation.

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 clearly states the tool's purpose: 'Auto-pick cheapest LLM (GPT-4 / Claude / Haiku) that meets your quality bar'. It specifies the action (auto-pick), resources (LLMs), and criteria (cheapest, meets quality bar). However, it doesn't explicitly differentiate from sibling tools like 'llm_pricing' or 'ai_cost_calculator', which prevents a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools related to AI/LLM operations (llm_pricing, ai_cost_calculator, ai_provider_status, etc.), there's no indication of when this routing tool is appropriate versus those other tools for cost, status, or other LLM-related tasks.

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