Based on a tutorial by Cole Medin
If you’re tired of AI coding assistants hallucinating when working with specific frameworks, you’re not alone. We’ve all been there – asking ChatGPT or Claude about a particular tool only to get outdated or completely wrong information.
That’s exactly why I’m excited to share this breakdown of Context 7, a game-changing tool that gives AI coding assistants instant access to up-to-date documentation for nearly 1,900 frameworks and tools. This summary will help you understand how Context 7 works and whether it’s worth integrating into your development workflow.
Quick Navigation
What is Context 7 and Why It Matters
Context 7 is a revolutionary tool that solves one of the biggest problems with AI coding assistants: hallucination when dealing with specific frameworks and tools. Instead of getting generic or outdated responses, Context 7 provides instant RAG (Retrieval Augmented Generation) capabilities.
Key Benefits:
- Access to documentation for 1,856+ different tools and frameworks
- Completely up-to-date documentation that can be refreshed at any time
- Well-structured examples rather than just raw text dumps
- Works as an MCP server with popular AI coding assistants
- Free to use
My Take:
This feels like one of those “why didn’t this exist before?” moments. The fact that it’s free and covers nearly 1,900 frameworks is honestly mind-blowing. If you’re using AI coding assistants regularly, this should be an instant addition to your toolkit.
Exploring the Context 7 Platform
The Context 7 homepage showcases its impressive collection of documentation. You’ll find everything from Next.js and MongoDB to Supabase, Pydantic AI, React, and LangGraph – basically every major framework you’d want to work with.
Platform Features:
- Individual documentation pages for each framework with metadata
- Token count information for efficient RAG operations
- Built-in search functionality to test RAG queries
- Curated documentation snippets with practical examples
- Regular updates to keep documentation current
What sets Context 7 apart from basic documentation dumps is how they structure their content. Instead of just throwing entire documentation into a single file, they’ve curated individual components as snippets that LLMs can parse effectively. Each component includes examples, which is crucial for helping AI assistants code reliably.
My Take:
The example-heavy approach is brilliant. I’ve found that giving LLMs concrete examples dramatically improves their coding accuracy, and Context 7 has built their entire platform around this principle.
Setting Up the MCP Server
The real power of Context 7 comes through its MCP (Model Context Protocol) server, which integrates directly with AI coding assistants like Cursor and Windsurf.
What You Get Without Context 7:
- Hallucinated APIs that don’t exist
- Generic answers from outdated package versions
- Inconsistent and unreliable code suggestions
What Context 7 Provides:
- Up-to-date, version-specific documentation
- Real code examples straight from the source
- Contextually relevant information based on your specific queries
Installation Process
Setting up Context 7 is straightforward. For Windsurf users, you simply click the hammer icon for MCP servers, configure the settings, and paste the JSON configuration from the Context 7 GitHub repository.
{
"context7": {
"command": "npx",
"args": ["@context7/mcp-server"],
"env": {}
}
}
Understanding the Two Tools
Context 7 provides two main functions through its MCP server:
Available Tools:
- Resolve Library ID: Searches for relevant documentation pages and returns the specific ID needed for RAG
- Get Library Docs: Performs actual RAG using the library ID and a topic search term
My Take:
The two-step process is smart – it allows the AI assistant to reason about which documentation it needs before fetching it, making the whole process more efficient and targeted.
Building an AI Agent with Context 7
To demonstrate Context 7’s power, the tutorial shows building an AI agent using Pydantic AI that itself can use Context 7 for RAG – a meta approach that really showcases the tool’s capabilities.
Demo Project Features:
- AI agent framework built with Pydantic AI
- Context 7 integration as an MCP server
- Environment variables for flexible model configuration
- Command-line interface for easy interaction
- Ability to work with any model (OpenRouter, Ollama, Gemini, OpenAI)
Global Rules Configuration
The tutorial demonstrates setting up global rules to optimize Context 7 usage:
## Context 7 Usage Guidelines
- Start with 5,000 tokens for documentation search
- Increase to 20,000 tokens if more context needed
- Use Brave MCP server as fallback when necessary
- Prioritize example-heavy documentation snippets
My Take:
The configurable token limits are brilliant. Different frameworks require different amounts of context, and being able to adjust this dynamically makes the system much more efficient than fixed-size retrieval.
Real-World Results and Testing
The demo shows Context 7 in action, successfully creating a functional AI agent with proper documentation retrieval. The tool performs exactly as advertised, making multiple strategic calls to resolve library IDs and fetch relevant documentation.
Observed Functionality:
- Successful resolution of Pydantic AI library documentation
- Intelligent token allocation (20,000 tokens for comprehensive retrieval)
- High-quality code generation with proper examples
- Integration with fallback search capabilities
- Clean command-line interface with conversation history
Testing the Agent
The final test demonstrates the agent successfully accessing Supabase documentation through Context 7 and providing accurate, up-to-date code examples for real-time database changes.
// Example output from Context 7 RAG query
const subscription = supabase
.channel('channel-id')
.on('postgres_changes',
{ event: '*', schema: 'public', table: 'your_table' },
(payload) => {
console.log('Change detected:', payload);
}
)
.subscribe();
What Makes This Powerful:
- No need to manually scrape and embed documentation
- Instant access to nearly 1,900 documentation sources
- Always up-to-date information
- Works with any existing AI coding workflow
- Dramatically reduces hallucination in framework-specific queries
My Take:
This is genuinely game-changing for AI-assisted development. Instead of building and maintaining your own RAG system for documentation, you get instant access to professionally curated, example-rich documentation for virtually every framework you’d want to use. It’s one of those tools that immediately becomes indispensable once you try it.
