Convex MCP Docs Server - PRD

Convex MCP Docs Server - Product Requirements Document

Executive Summary

The Convex MCP Docs Server represents the next evolution in AI-assisted development for Convex applications. Building upon the successful foundation of convex-evals, this Model Context Protocol (MCP) server delivers dynamic, context-aware best practices directly to AI coding agents, ensuring consistently high-quality code generation that adapts to each project’s unique requirements.

User Stories

Primary User Stories

As an AI-assisted developer, I want my coding agent to automatically know my project’s validation library, auth system, and components so that generated code follows my exact stack without manual configuration.

As a team lead, I want consistent patterns across all team members so that code reviews focus on business logic rather than style and structural issues.

As a developer using Convex components, I want my AI to understand component-specific patterns so that integrations are implemented correctly the first time.

As a maintainer of multiple projects, I want best practices to update automatically so that all my projects benefit from new patterns without manual intervention.

Secondary User Stories

As a new Convex developer, I want my AI to teach me best practices through generated code so that I learn while building.

As an organization scaling Convex usage, I want standardized patterns across projects so that developers can move between codebases seamlessly.

As a contributor to the Convex ecosystem, I want a way to share patterns so that the community benefits from accumulated knowledge.

Functional Requirements

Core Features

1. Project Configuration Detection

Auto-detection from package.json, imports, and existing code patterns
Manual configuration via CLI flags and config files
Interactive setup wizard for new projects
Configuration validation with helpful error messages

2. Dynamic Rule Compilation

Source integration from official docs, components, Stack articles, and community patterns
Context-aware filtering to load only relevant guidelines for the detected stack
Model optimization with format-specific output (JSON for GPT-4, XML for Claude, etc.)
Real-time updates when new patterns are published

3. MCP Protocol Implementation

Tool interface providing getBestPractice, generatePattern, validateCode, and searchDocs methods
Streaming updates for real-time rule synchronization
Caching layer with intelligent cache invalidation
Performance monitoring and optimization

4. Editor Integration

One-click setup for Cursor with deep integration
Manual configuration support for Windsurf, VS Code, and Claude Desktop
Better T Stack integration with automatic inclusion in new projects
Configuration templates for different development environments

Technical Requirements

Architecture

interface ProjectConfig {
  validation: 'zod' | 'valibot' | 'convex-native';
  auth: 'clerk' | 'better-auth' | 'auth0' | 'custom';
  components: string[]; // ['polar', 'resend', 'ai-agent', etc.]
  model: 'anthropic' | 'openai' | 'google' | 'auto';
  features: {
    helpers: boolean;      // Use convex-helpers
    tanstack: boolean;     // TanStack Query integration
    testing: boolean;      // Include testing patterns
  };
}

Performance Targets

Startup time: <500ms
Rule compilation: <100ms
Memory usage: <50MB
Cache hit rate: >90%

MCP Tools

interface ConvexDocsMCPTools {
  getBestPractice(topic: string): BestPractice;
  generatePattern(type: 'query' | 'mutation' | 'action', context: Context): Code;
  validateCode(code: string): ValidationResult;
  getComponentUsage(component: string): UsageExample[];
  searchDocs(query: string): SearchResult[];
  getProjectConfig(): ProjectConfig;
  updateConfig(updates: Partial<ProjectConfig>): void;
}

Non-Functional Requirements

Performance

Initial load time under 500ms
Rule queries respond in under 100ms
Memory footprint under 50MB
99.9% uptime for rule retrieval

Scalability

Support for 10,000+ concurrent AI agents
Handle 1M+ rule queries per day
Graceful degradation under high load
CDN distribution for global performance

Security

No sensitive data transmission
Local rule caching for privacy
Secure pattern validation pipeline
Safe community contribution system

Compatibility

Node.js 18+ support
Works with all major MCP-compatible editors
Cross-platform (Windows, macOS, Linux)
Backwards compatibility with convex-evals

User Experience Requirements

Installation & Setup

One-command install: npx @convex/mcp-docs-server install --editor=cursor
Automatic detection of project configuration in 90% of cases
Clear error messages with actionable resolution steps
Migration assistant from existing convex-evals setups

Runtime Experience

Invisible operation - no user intervention required after setup
Immediate benefits - better code generation from the first query
Consistent patterns across all AI interactions
Real-time updates without service interruption

Developer Experience

Zero configuration for standard setups
Flexible customization for complex projects
Observable behavior through debug logging
Community contribution pathways

Success Metrics

Adoption Metrics

50% of Cursor users adopt within 3 months of launch
10,000+ projects using MCP docs server within 6 months
5x reduction in setup time vs manual rule management

Quality Metrics

90% reduction in common Convex anti-patterns in generated code
2x faster development velocity measured by feature completion time
95% satisfaction rate from developers in quarterly surveys

Performance Metrics

<100ms average response time for rule queries
>99% uptime for the MCP server
Zero manual updates required for rule synchronization

Technical Implementation

Phase 1: Foundation (Weeks 1-4)

Core MCP server implementation
Basic rule compilation engine
Project configuration detection
Cursor integration and testing

Phase 2: Content Pipeline (Weeks 5-8)

Auto-indexing system for docs and articles
Component documentation integration
Rule optimization and caching
Multi-model format support

Phase 3: Advanced Features (Weeks 9-12)

Better T Stack integration
Community contribution system
Advanced analytics and telemetry
Performance optimization

Phase 4: Polish & Launch (Weeks 13-16)

Comprehensive documentation
Migration tools from convex-evals
Public launch and marketing
Community onboarding programs

Risk Mitigation

Technical Risks

MCP protocol changes: Abstract protocol implementation to minimize impact
Performance degradation: Implement aggressive caching and CDN distribution
AI model compatibility: Build model-specific adapters for different response formats

Adoption Risks

Learning curve: Provide comprehensive tutorials and one-click setup tools
Migration friction: Build automated migration tools and compatibility modes
Integration complexity: Maintain extensive testing matrix across editors and platforms

Content Risks

Pattern quality: Establish rigorous validation pipeline using convex-evals testing
Community contributions: Implement moderation and testing before pattern inclusion
Documentation drift: Automated synchronization with official Convex documentation

Competitive Analysis

Current Landscape

Supabase: Static code snippets with no AI-specific tooling
Firebase: Basic documentation integration, no dynamic patterns
Prisma: Code generation focused, not AI-optimized
Drizzle: Manual documentation approach

Convex Advantages

First dynamic MCP implementation for backend platforms
Component ecosystem provides rich pattern library
Auto-updating best practices eliminate maintenance burden
Project-aware intelligence adapts to specific technology choices

Future Vision

Q4 2025

GitHub Copilot Workspace integration
Custom team-specific pattern sets
Advanced pattern contribution workflows

Q1 2026

AI-powered pattern discovery from community code
Automatic anti-pattern detection and suggestions
Cross-project pattern learning and optimization

Q2 2026

Full IDE integration beyond AI assistance
Real-time pattern validation in editors
Integration with component marketplace

Migration from convex-evals

Compatibility Approach

Side-by-side operation during transition period
Automatic import of existing convex-evals rules
Gradual migration path with user control
Feature parity before any deprecation

Benefits of Migration

Eliminate manual rule updates across projects
Gain project-specific optimization capabilities
Achieve stronger AI compliance through MCP protocol
Access to broader pattern library including components

Migration Timeline

Month 1: Launch with convex-evals compatibility mode
Month 3: Provide migration tools and documentation
Month 6: Begin gentle migration prompts for users
Month 12: Consider convex-evals maintenance mode (not deprecation)

Call to Action

The Convex MCP Docs Server represents a paradigm shift in how developers interact with backend platforms through AI. By building this feature, Convex will:

Establish market leadership in AI-assisted backend development
Reduce developer support burden through consistently better generated code
Accelerate platform adoption by lowering the learning curve
Create a competitive moat through superior developer experience

This is not just an improvement—it’s the definition of what an AI-first backend platform should be.