Prompt Refiner MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Prompt Refiner MCP ServerRefine my idea: 'build a task manager app'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Prompt Refiner MCP Server
A Model Context Protocol server that helps systematically refine vague ideas into well-structured prompts through guided clarification.
Philosophy
This server follows the Sequential Thinking pattern:
Server provides STRUCTURE - tracks refinement steps, formats output, applies templates
Claude provides INTELLIGENCE - analyzes ideas, asks questions, decides when complete
Single focused tool with clear workflow
Visual progress feedback via colored stderr output
Related MCP server: MCP Prompt Cleaner
Installation
npm install
npm run buildUsage
Run Locally
node dist/index.jsAdd to Claude Desktop
Edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"promptrefiner": {
"command": "node",
"args": ["/Users/nmelo/Desktop/Projects/prompter/dist/index.js"]
}
}
}Docker (Optional)
docker build -t promptrefiner .
docker run -i promptrefinerHow It Works
The Tool: promptrefiner
Three modes:
Start - Begin refinement with original idea
{ originalIdea: "I want to build an API" }Clarify - Add clarifications for specific aspects
{ aspect: "purpose", clarification: "REST API for user authentication with JWT tokens" }Export - Generate refined prompt using a template
{ exportTemplate: "technical-spec", refinementComplete: true }
Aspects to Clarify
purpose (critical) - What is this for? What problem does it solve?
audience (critical) - Who will use this? Skill level?
constraints (important) - Requirements, limitations, technical constraints
context (important) - Where/when/how will this be used?
success (important) - How is success measured? What does "done" look like?
scope (helpful) - What's in/out of scope?
style (optional) - Tone, format, style preferences
Templates
detailed-structured - Comprehensive with sections (Purpose, Audience, Requirements, etc.)
concise-bullets - Brief bullet-point format
technical-spec - Formal specification style
conversational - Natural language paragraph
minimal - Just the essentials
Example Session
User: Help me refine this idea: "I want to build an API"
Claude: [calls promptrefiner with originalIdea]
π― Starting Refinement
ββββββββββββββββββββββββββββββββββββββββββ
β Original Idea β
ββββββββββββββββββββββββββββββββββββββββββ€
β I want to build an API β
ββββββββββββββββββββββββββββββββββββββββββ
Claude: Let me ask some clarifying questions:
- What will the API do? What's its main purpose?
- Who will use this API?
- Are there any technical requirements?
User: It's for user authentication. Developers will integrate it.
Needs JWT tokens and should handle 1000 requests/sec.
Claude: [calls promptrefiner with aspect="purpose", clarification="..."]
π Step 2: Clarifying Purpose
ββββββββββββββββββββββββββββββββββββββββββ
β REST API for user authentication β
β using JWT tokens β
ββββββββββββββββββββββββββββββββββββββββββ
Step 2/5 | Covered: 1 aspect
[... more clarifications ...]
Claude: [calls promptrefiner with exportTemplate="technical-spec", refinementComplete=true]
β¨ Step 5: Exported as technical-spec
ββββββββββββββββββββββββββββββββββββββββββ
β Template: technical-spec β
β Aspects covered: 3 β
β Total steps: 5 β
ββββββββββββββββββββββββββββββββββββββββββ
Claude: Here's your refined prompt:
# Specification
**Purpose**: User authentication API using JWT tokens, handling login,
logout, and token refresh
**Target Users**: Developers integrating authentication into their applications
**Technical Requirements**: Handle 1000 requests per second, JWT token-based
authentication
**Deployment Context**: Not specified
**Acceptance Criteria**: Functional implementationFeatures
β Structured workflow - Systematic refinement process β Multiple clarifications - Can clarify same aspect multiple times (concatenated) β Visual progress - Colored console output with progress tracking β Flexible templates - 5 built-in export formats β Type-safe - Full TypeScript with strict validation β oneOf schema - Enforces correct tool usage modes
Environment Variables
DISABLE_PROGRESS_LOGGING=true- Disable colored stderr output
Architecture
346 lines of TypeScript
Single tool with oneOf validation
5 template functions using template literals
State tracking via refinement history array
Duplicate handling - Multiple clarifications per aspect concatenated with
\n\n
Development
npm run watch # Watch mode during development
npm run build # Build for productionLicense
MIT
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseBqualityDmaintenanceIntelligently engineers and optimizes prompts for Claude Code with automatic language detection, task type recognition, and interactive refinement capabilities. Works entirely offline without external API dependencies to transform natural language requests into structured, Claude Code-optimized prompts.Last updated412MIT
- Alicense-qualityDmaintenanceEnhances and cleans raw prompts using AI to make them more clear, actionable, and effective. Provides quality assessment, suggestions, and supports both general and code-specific optimization modes.Last updated1MIT
- Alicense-qualityDmaintenanceTransforms high-level business intents into structured Data Product Requirement Prompts through AI-powered conversational refinement. Guides users through clarifying questions to gather comprehensive requirements for automated Business Intelligence dashboard generation.Last updated1Apache 2.0
- FlicenseAqualityCmaintenanceRefines and improves AI prompts using workspace-aware context from your project's tech stack, structure, and dependencies. Includes tools to analyze prompt quality and generate well-structured prompts from raw ideas.Last updated42095
Related MCP Connectors
Turns vague automation requests into tool stacks, prompts, QA checks, and human boundaries.
Find your AI agent's likely failure mode, get runtime settings, and clarify ambiguous prompts.
Generate tailored quality criteria and scoring guides from your task descriptions. Refine objectivβ¦
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/nmelo/prompter-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server