lazymac-mcp

prompt_optimizer

Optimize AI prompts to reduce costs, improve quality, and ensure consistency across applications.

Instructions

Optimize prompts for cost, quality, and consistency

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": "prompt_optimizer"
  }
}

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

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions optimizing for 'cost, quality, and consistency' but does not explain how this is achieved (e.g., via API calls, local processing, or external services), what the output looks like, or any limitations like rate limits or authentication needs. This is inadequate for a tool with potential complexity.

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?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and easy to parse, though it could benefit from slightly more detail to improve clarity without sacrificing brevity.

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 lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns, how optimizations are applied, or any behavioral traits. For a tool with potential complexity (implied by optimizing multiple aspects), this minimal description fails to provide sufficient context for effective use by an agent.

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 as free-form parameters passed in queries or JSON. The description does not add any semantic details beyond this, such as example parameters or expected fields. With high schema coverage, the baseline score of 3 is appropriate, as the description provides no extra parameter context.

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

Purpose3/5

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

The description 'Optimize prompts for cost, quality, and consistency' states a clear purpose with a specific verb ('optimize') and target ('prompts'), but it lacks detail on what optimization entails or how it differs from sibling tools like 'prompt_shield' or 'text_analysis'. It's vague about the mechanism (e.g., whether it suggests edits, analyzes metrics, or generates alternatives).

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclusions, and it fails to differentiate from sibling tools such as 'prompt_shield' (which might protect prompts) or 'text_analysis' (which could analyze prompts). This leaves the agent without direction on appropriate usage scenarios.

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