Supercharge Your AI Workflow with Context-7
As developers, we’ve all been there: you’re deep in a coding session with Claude or another AI assistant, trying to explain your project context, and find yourself copy-pasting documentation snippets, code examples, and README files. It’s like trying to give someone directions to your house by describing every landmark along the way, every single time. What if your AI assistant could just know your project inside and out?
Enter Context 7 – an MCP (Model Context Protocol) server that transforms how AI assistants understand your development environment. Think of it as giving your AI assistant a permanent residency in your codebase, with full access to all your documentation, code, and project knowledge.
What is Context 7?
Context 7 is an MCP server that creates intelligent, searchable knowledge bases from documentation, codebases, and web content. Unlike traditional chatbots that forget everything between conversations, MCP servers like Context 7 provide persistent, rich context that AI assistants can access whenever they need it.
The Model Context Protocol (MCP) is like having a universal translator between AI assistants and your development tools. Instead of manually feeding information to your AI, MCP servers provide structured, real-time access to the resources your AI needs to give you accurate, contextual help.
Context 7 excels at:
- Documentation indexing: Transform any documentation site into AI-accessible knowledge
- Codebase understanding: Provide semantic search across entire repositories
- Persistent context: Your AI remembers your project setup across all conversations
- Semantic search: Find relevant information based on intent, not just keywords
Why Context 7 Changes Everything
Traditional AI assistance for development is like having a brilliant consultant who’s never seen your project before – every conversation starts from scratch. With Context 7 as an MCP server, your AI assistant becomes like a seasoned team member who knows your codebase, understands your documentation, and can provide contextual advice without you having to explain everything repeatedly.
Here’s what makes it revolutionary:
1. Persistent Project Knowledge
Your AI assistant remembers your project structure, coding patterns, and documentation. No more re-explaining your architecture in every conversation.
2. Semantic Code Understanding
Ask “How do I handle loading states in this project?” and Context 7 will surface relevant patterns from your codebase, even if they use different terminology.
3. Live Documentation Access
Instead of copying and pasting docs, your AI can directly reference the latest documentation and provide accurate, up-to-date guidance.
Setting Up Context 7 MCP Server
Context 7 runs as an MCP server that connects to AI assistants like Claude. Here’s how to get it running:
// Install Context 7 MCP server
npm install -g @upstash/context7-mcp
// Or using your preferred package manager
pnpm add -g @upstash/context7-mcp{
"mcpServers": {
"context7": {
"command": "context7-mcp",
"args": ["--config", "./context7-config.json"],
"env": {
"CONTEXT7_API_KEY": "your-api-key-here"
}
}
}
}{
"knowledgeBases": [
{
"name": "project-docs",
"sources": [
{
"type": "repository",
"url": "https://github.com/your-org/your-repo",
"branch": "main"
},
{
"type": "documentation",
"url": "https://your-docs-site.com",
"crawlDepth": 3
}
]
}
]
}Real-World Example: TanStack Query Integration
Let’s explore a concrete example using TanStack Query. Instead of manually copying documentation every time you need help, Context 7 makes the entire TanStack ecosystem available to your AI assistant.
Step 1: Configuring TanStack Documentation Access
{
"knowledgeBases": [
{
"name": "tanstack-ecosystem",
"description": "Complete TanStack Query v5 documentation and examples",
"sources": [
{
"type": "documentation",
"url": "https://tanstack.com/query/v5/docs",
"includePatterns": [
"/docs/framework/react/*",
"/docs/guides/*",
"/docs/reference/*"
],
"crawlDepth": 4
},
{
"type": "repository",
"url": "https://github.com/TanStack/query",
"paths": ["examples/", "docs/", "packages/react-query/src/"]
}
]
}
]
}Step 2: AI Assistant with Full Context
Once Context 7 is running, your conversations with Claude become incredibly rich. Instead of this:
Before Context 7:
You: "How do I implement optimistic updates with TanStack Query?"
Claude: "I'd need to see your current setup and know which version of TanStack Query you're using. Generally, you'd use the onMutate option..."With Context 7:
You: "How do I implement optimistic updates with TanStack Query in my current project?"
Claude: "Looking at your project setup and the TanStack Query v5 documentation, I can see you're using React 18 with TypeScript. Based on your existing query patterns, here's how to implement optimistic updates..."Step 3: Context-Aware Code Generation
The MCP server allows for incredibly sophisticated interactions:
// Your AI assistant can now generate code that fits your project perfectly
import { useMutation, useQueryClient } from "@tanstack/react-query"
import type { User, UpdateUserRequest } from "@/types/user"
export const useUpdateUser = () => {
const queryClient = useQueryClient()
return useMutation({
mutationFn: async (data: UpdateUserRequest): Promise<User> => {
// Implementation based on your existing API patterns
const response = await fetch(`/api/users/${data.id}`, {
method: "PATCH",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(data)
})
if (!response.ok) {
throw new Error("Failed to update user")
}
return response.json()
},
onMutate: async (newUserData) => {
// Cancel outgoing refetches so they don't overwrite optimistic update
await queryClient.cancelQueries({ queryKey: ["users", newUserData.id] })
// Snapshot previous value
const previousUser = queryClient.getQueryData<User>(["users", newUserData.id])
// Optimistically update to new value
queryClient.setQueryData<User>(["users", newUserData.id], (old) => ({
...old!,
...newUserData
}))
return { previousUser }
},
onError: (err, newUserData, context) => {
// Rollback on error
queryClient.setQueryData(
["users", newUserData.id],
context?.previousUser
)
},
onSettled: (data, error, userData) => {
// Always refetch after error or success
queryClient.invalidateQueries({ queryKey: ["users", userData.id] })
}
})
}Notice how the AI assistant can generate code that:
- Uses your exact TypeScript types
- Follows your project’s API patterns
- Implements proper error handling based on your existing code
- Uses the latest TanStack Query v5 patterns
Git and GitHub Workflow Enhancement
Since you’re working on improving your Git and GitHub proficiency, Context 7 can make your AI assistant an expert in your repository workflows:
{
"knowledgeBases": [
{
"name": "git-workflows",
"sources": [
{
"type": "repository",
"url": "https://github.com/your-org/your-repo",
"paths": [".github/", "docs/contributing.md", "README.md"]
},
{
"type": "documentation",
"url": "https://docs.github.com/en/get-started/quickstart/github-flow"
},
{
"type": "repository",
"url": "https://github.com/conventional-commits/conventionalcommits.org"
}
]
}
]
}Now your AI assistant understands your team’s specific Git workflows:
// Your AI can now provide project-specific Git advice:
// "What's the proper way to create a feature branch in this project?"
// AI Response (with full context):
// "Based on your .github/workflows and contributing guidelines, you should:
//
// 1. Create a branch following your naming convention: feature/TICKET-123-short-description
// 2. Use conventional commits as specified in your .gitmessage template
// 3. Ensure your branch passes the pre-commit hooks defined in .pre-commit-config.yaml
// 4. Follow the PR template in .github/pull_request_template.md"
// The AI knows your exact workflow because Context 7 provides that context!Advanced MCP Server Features
Tool Integration with Biome
Since you use Biome for code formatting, Context 7 can make your AI assistant aware of your specific linting rules:
{
"knowledgeBases": [
{
"name": "dev-tooling",
"sources": [
{
"type": "file",
"path": "./biome.json"
},
{
"type": "file",
"path": "./tsconfig.json"
},
{
"type": "documentation",
"url": "https://biomejs.dev/guides/"
}
]
}
]
}Now when you ask for code help, your AI assistant automatically follows your Biome configuration:
// AI generates code that automatically follows your Biome rules:
import type { FC } from "react" // Type-only imports as per Biome config
import type { User } from "@/types/user"
interface UserCardProps {
readonly user: User
readonly onEdit: (id: string) => void
}
export const UserCard: FC<UserCardProps> = ({ user, onEdit }) => {
return (
<div className="user-card">
<h3>{user.name}</h3>
<button type="button" onClick={() => onEdit(user.id)}>
Edit User
</button>
</div>
)
}Hono Backend Integration
For your Hono preference over Express, Context 7 can maintain context about your API patterns:
// With Context 7, your AI knows your Hono patterns:
import { Hono } from "hono"
import { validator } from "hono/validator"
import { z } from "zod"
import type { Variables } from "@/types/hono"
const updateUserSchema = z.object({
name: z.string().min(1),
email: z.string().email()
})
export const usersRoute = new Hono<{ Variables: Variables }>()
.patch(
"/users/:id",
validator("json", (value, c) => {
const parsed = updateUserSchema.safeParse(value)
if (!parsed.success) {
return c.json({ error: "Invalid input" }, 400)
}
return parsed.data
}),
async (c) => {
const { id } = c.req.param()
const data = c.req.valid("json")
// Your AI assistant knows your exact database patterns too!
const updatedUser = await db.user.update({
where: { id },
data
})
return c.json(updatedUser)
}
)Best Practices for MCP Server Usage
1. Organize Knowledge Bases by Scope
Structure your knowledge bases strategically:
- Framework Documentation: React, TanStack, Hono
- Project Context: Your specific codebase and patterns
- Development Standards: Biome configs, Git workflows, TypeScript configs
2. Keep Context Fresh
MCP servers can automatically sync with your repositories, ensuring your AI always has the latest context.
3. Use Semantic Queries
With Context 7 providing rich context, you can ask more natural questions:
- ❌ “Show me the useQuery hook syntax”
- ✅ “How should I fetch user data in this component following our project patterns?”
The MCP Advantage
The Model Context Protocol represents a fundamental shift in AI-assisted development. Instead of AI assistants being blank slates that require constant context-setting, MCP servers like Context 7 create persistent, intelligent environments where your AI truly understands your development world.
This isn’t just about convenience – it’s about unlocking entirely new workflows:
- Code Reviews: Your AI can review PRs with full project context
- Architecture Decisions: Get advice that considers your existing patterns
- Documentation: Generate docs that match your project’s style and structure
- Debugging: AI assistance that understands your specific tech stack and patterns
Getting Started with Context 7
Ready to transform your AI development workflow? Here’s your action plan:
- Install Context 7 as an MCP server following the setup instructions
- Configure knowledge bases for your key documentation and codebases
- Connect to your AI assistant (like Claude) via MCP configuration
- Start conversations with rich, persistent context
- Iterate and expand your knowledge bases as your projects grow
The beauty of MCP servers is that the initial setup pays dividends immediately. Every conversation becomes more productive, every piece of generated code becomes more accurate, and your AI assistant becomes a true development partner rather than just a helpful tool.
Context 7 isn’t just another development tool – it’s the bridge between AI assistance and true project understanding. Once you experience development with persistent, rich context, you’ll wonder how you ever coded without it.
Ready to get started? Check out the Context 7 repository and visit context7.com/about to learn more about implementing MCP servers in your development workflow.