Feature-Discussion MCP Server
This server facilitates AI-driven feature discussions, acting as an AI lead developer to guide implementation and architecture decisions.
Start new feature discussions with titles (
begin_feature_discussion)Provide input to ongoing discussions (
provide_feature_input)Receive expert guidance and recommendations on implementation approaches
Maintain persistent memory of discussions, decisions, and dependencies
Track feature evolution throughout the project lifecycle
Get best practices and technology stack recommendations
Debug interactions using the MCP Inspector
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., "@Feature-Discussion MCP Serverwe need to add user authentication to our React app - what's the best approach?"
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.
feature-discussion MCP Server
A TypeScript-based Model Context Protocol (MCP) server that facilitates intelligent feature discussions between developers and AI. This server acts as an AI lead developer, providing guidance on feature implementation, maintaining context of discussions, and helping teams make informed architectural decisions.
This server provides:
Interactive discussions about feature implementation and architecture
Persistent memory of feature discussions and decisions
Intelligent guidance on development approaches and best practices
Context-aware recommendations based on project history
Features
AI Lead Developer Interface
Engage in natural discussions about feature requirements
Get expert guidance on implementation approaches
Receive architectural recommendations
Maintain context across multiple discussions
Feature Memory Management
Persistent storage of feature discussions
Track feature evolution and decisions
Reference previous discussions for context
Link related features and dependencies
Development Guidance
Best practices recommendations
Implementation strategy suggestions
Architecture pattern recommendations
Technology stack considerations
Context Management
Maintain project-wide feature context
Track dependencies between features
Store architectural decisions
Remember previous discussion outcomes
Related MCP server: AI Develop Assistant
Installation
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"feature-discussion": {
"command": "/path/to/feature-discussion/build/index.js"
}
}
}Development
Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchDebugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
npm run inspectorThe Inspector will provide a URL to access debugging tools in your browser.
Contributing
We welcome contributions! Please see our Contributing Guidelines for details on how to get started, and our Code of Conduct for community guidelines.
License
This project is licensed under the MIT License - see the LICENSE file for details.
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
- AlicenseBqualityDmaintenanceEnables AI-guided spec-driven development workflow that transforms ideas into implementation through structured stages: goal collection, requirements gathering in EARS format, technical design documentation, task planning, and systematic code execution.Last updated91521MIT
- Alicense-qualityDmaintenanceAssists AI developers with intelligent requirement analysis and architecture design through guided clarification questions, branch-aware management, and automated architecture generation with persistent storage.Last updated133MIT
- Flicense-qualityDmaintenanceEnables AI development tools to maintain context across chat sessions with automatic branching, progress tracking, and TODO management for different tasks.Last updated
- Flicense-quality-maintenanceImplements a structured development workflow for LLM-based coding with feature clarification, PRD generation, phased development, and task tracking. Guides LLMs through organized feature development from requirements gathering to completion with document storage and progress monitoring.Last updated80
Related MCP Connectors
Adaptive plan/build/review cycles for AI coding assistants, persisted across sessions.
The project brain for AI coding agents — memory, decisions, sprints, knowledge base via MCP.
AI Agent with Architectural Memory. Impact analysis (free), tests and code from the graph (pro).
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/squirrelogic/mcp-feature-discussion'
If you have feedback or need assistance with the MCP directory API, please join our Discord server