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 "Deploy 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 Server we 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.
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
Engage in natural discussions about feature requirements
Get expert guidance on implementation approaches
Receive architectural recommendations
Maintain context across multiple discussions
Persistent storage of feature discussions
Track feature evolution and decisions
Reference previous discussions for context
Link related features and dependencies
Best practices recommendations
Implementation strategy suggestions
Architecture pattern recommendations
Technology stack considerations
Maintain project-wide feature context
Track dependencies between features
Store architectural decisions
Remember previous discussion outcomes
Related MCP server: Context Management System
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"
}
}
}Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchSince 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.
We welcome contributions! Please see our Contributing Guidelines for details on how to get started, and our Code of Conduct for community guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.
Start a new feature discussion
Inputs: title (required).
Provide information for the current feature discussion prompt
Inputs: featureId (required), response (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 2 tools
The two tools have clearly distinct purposes: 'begin_feature_discussion' initiates a new discussion, while 'provide_feature_input' adds information to an existing one. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun pattern with snake_case naming. 'begin_feature_discussion' and 'provide_feature_input' use clear, descriptive verbs ('begin' and 'provide') paired with the same noun phrase ('feature_discussion' or 'feature_input'), ensuring predictability.
With only 2 tools, the server feels thin for a feature discussion domain. It lacks essential operations like retrieving, updating, or closing discussions, which limits its utility and suggests an incomplete scope.
The tool surface is severely incomplete for feature discussions. It covers starting a discussion and providing input, but missing critical operations such as getting discussion details, listing discussions, updating status, or adding comments, which will likely cause agent failures in real workflows.
AI colleagues that keep your standards, your project and their reasoning between sessions
Capture feature requests and bug reports from chat into a searchable, AI-categorized backlog.
Generate architecture docs, implementation plans, and dev tickets from your project requirements.
Bring your saved preferences, project decisions, and useful context into your next AI conversation.
