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Prompt Ops MCP

by heyjustinai
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Prompt Ops MCP

A streamlined Model Context Protocol (MCP) server that optimizes prompts using meta-prompting techniques. This server can be easily integrated into Cursor and other MCP-compatible tools to enhance prompt quality and effectiveness.

Features

  • Two-Turn Prompt Optimization: Transform basic prompts into sophisticated, structured requests using a simple two-turn approach

  • Meta-Prompting Technique: Leverages the LLM's capabilities to apply optimization guidelines

  • MCP Integration: Seamlessly integrates with Cursor and other MCP-compatible tools

  • TypeScript: Built with TypeScript for type safety and better development experience

Related MCP server: PromptTuner MCP

Installation

Via NPM (Recommended)

npm install -g prompt-ops-mcp

From Source

git clone <repository-url>
cd prompt-ops-mcp
npm install
npm run build

Usage

Integration with Cursor

Add the following to your Cursor MCP settings:

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "npx",
      "args": ["prompt-ops-mcp"]
    }
  }
}

Direct Usage

# Run the server
npx prompt-ops-mcp

# Or if installed globally
prompt-ops-mcp

How It Works: Two-Turn Optimization

The prompt optimizer uses a simple two-turn approach:

  1. Turn 1: Provide your original prompt → Receive optimization guidelines

  2. Turn 2: Provide the optimized prompt → Get it ready for use

Available Tool: promptenhancer

Parameters:

  • originalPrompt: The prompt you want to optimize (for Turn 1)

  • optimizedPrompt: The optimized prompt created by following the guidelines (for Turn 2)

Example Usage (Turn 1):

@prompt-ops promptenhancer {"originalPrompt": "Write a Python function to calculate fibonacci numbers"}

Example Usage (Turn 2):

@prompt-ops promptenhancer {"optimizedPrompt": "Your optimized prompt here..."}

Optimization Guidelines

The meta-prompting framework includes guidance for:

  1. Clarifying Intent and Scope: Making implicit requirements explicit

  2. Adding Structure and Organization: Breaking complex requests into clear sections

  3. Enhancing with Reasoning Elements: Including step-by-step thinking instructions

  4. Providing Context and Examples: Adding relevant background information

  5. Setting Quality Standards: Defining success criteria and constraints

Example Transformation

See example-two-turn.md for a complete example of the two-turn optimization process.

Development

Setup

git clone <repository-url>
cd prompt-ops-mcp
npm install

Development Scripts

# Run in development mode
npm run dev

# Build the project
npm run build

# Run tests
npm run test

# Lint code
npm run lint

# Format code
npm run format

Project Structure

src/
├── index.ts              # Main MCP server implementation
├── prompt-optimizer.ts   # Core prompt optimization logic
└── types.ts             # TypeScript type definitions

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Add tests for new functionality

  5. Run npm run lint and npm run format

  6. Submit a pull request

License

MIT License - see LICENSE file for details

Support

For issues and questions:

Changelog

v1.0.0

  • Initial release with two-turn prompt optimization

  • Full MCP integration support

Available Tools

1 tool
promptenhancerA

A prompt optimization tool that guides you through transforming basic prompts into comprehensive, well-structured prompts.

How it works:

  1. First call: Provide an originalPrompt to receive optimization guidelines

  2. The LLM follows the guidelines to create an optimized version

  3. Second call: Provide the optimizedPrompt to get it ready for use

This tool uses a meta-prompting approach where the LLM does the actual optimization work.

ParametersJSON Schema
NameRequiredDescriptionDefault
originalPromptNoThe original prompt you want to optimize
optimizedPromptNoThe optimized prompt created by following the guidelines

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the two-call workflow and meta-prompting approach, which adds useful behavioral context. However, it doesn't disclose important traits like whether this requires specific permissions, rate limits, error handling, or what the response format looks like. For a tool with no annotations, this leaves significant gaps in behavioral understanding.

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 well-structured with a clear purpose statement followed by a numbered 'How it works' section and a concluding explanation of the approach. It's appropriately sized and front-loaded with the most important information. The final sentence about meta-prompting could potentially be integrated more seamlessly, but overall it's efficient with minimal waste.

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?

Given the tool's complexity (two-step process with different parameter usage patterns), no annotations, and no output schema, the description provides a reasonable foundation but has significant gaps. It explains the workflow but doesn't cover what the tool returns, error conditions, or detailed behavioral expectations. For a tool with this level of complexity and no structured support, the description should do more to compensate.

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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds some context by explaining when each parameter should be used (originalPrompt for first call, optimizedPrompt for second call), but doesn't provide additional semantic meaning beyond what the schema already states. This meets the baseline expectation when schema coverage is high.

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 clearly states the tool's purpose: 'A prompt optimization tool that guides you through transforming basic prompts into comprehensive, well-structured prompts.' It specifies the verb ('guides you through transforming'), resource ('prompts'), and distinguishes it as a meta-prompting approach. With no sibling tools, this level of specificity is excellent.

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 provides clear usage context through the 'How it works' section, explaining the two-step process: first call with originalPrompt, second call with optimizedPrompt. However, it doesn't explicitly state when NOT to use this tool or mention alternatives, which prevents a perfect score. With no sibling tools, the guidance is adequate but could be more comprehensive.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedpromptenhancer
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'promptenhancer' has a clearly distinct and singular purpose: transforming basic prompts into optimized versions through a guided two-step process.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'promptenhancer' follows a clear and descriptive pattern, combining the target domain ('prompt') with the action ('enhancer'), and there are no other tools to cause inconsistency.

Tool Count2/5

The server has only one tool, which feels thin for a server named 'Prompt Ops MCP' that might imply broader prompt operations. While the tool is well-described, a single tool limits the scope and could indicate incomplete coverage, as agents might expect more functionalities like prompt analysis, validation, or management.

Completeness2/5

The tool set is severely incomplete for the implied domain of prompt operations. It only offers enhancement via a specific meta-prompting approach, lacking other essential operations such as prompt validation, versioning, comparison, or basic CRUD management. This gap will likely cause agent failures when broader prompt-related tasks are needed.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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