Cursor SDK vs Continue.dev (2026)

TL;DR

Cursor is an AI-first IDE (closed, VS Code fork) — you integrate with it through VS Code Extension API or .cursorrules files, not an npm SDK. Continue.dev is the open-source equivalent with actual npm packages (@continuedev/core) for building custom AI coding assistants. For adding AI coding features to a custom IDE: Continue.dev. For VS Code extensions that leverage Cursor users: standard VS Code Extension API + Cursor context.

Key Takeaways

• Cursor: AI-first VS Code fork, no public SDK — integrate via .cursorrules, VS Code extension API, and MCP
• Continue.dev: OSS VS Code/JetBrains extension, @continuedev/core npm package for custom integrations
• MCP (Model Context Protocol): The 2026 standard for giving AI context from your tools
• For npm package authors: Add Cursor/Copilot-friendly JSDoc, .cursorrules, and MCP server
• Custom AI coding tools: Continue.dev + custom context providers > trying to extend Cursor

Downloads

Cursor Integration: What's Actually Possible

Cursor integration surface area (2026):

1. .cursorrules file (project-level AI instructions):
   → Custom coding standards
   → Library/framework preferences
   → Code style guidelines
   → No npm package needed

2. VS Code Extension API:
   → Cursor is a VS Code fork — most VS Code extensions work
   → Use standard vscode module for LSP, diagnostics, commands
   → Cursor-specific: Cursor Tab (AI autocomplete) reads context from open files

3. MCP (Model Context Protocol):
   → Add Cursor → Settings → MCP servers
   → Your MCP server provides context/tools to Cursor's AI
   → This is the REAL way to integrate custom data sources with Cursor

4. No public Cursor SDK (as of 2026):
   → Cursor team has not published an npm package for external integration
   → Integration is through VS Code Extension API + MCP

# .cursorrules — project AI instructions (no npm needed):
# Place at project root or .cursor/rules/

You are an expert TypeScript developer working on a Next.js 15 App Router project.

## Stack
- Next.js 15 with App Router
- TypeScript strict mode
- Drizzle ORM with PostgreSQL
- shadcn/ui components
- Tailwind CSS v4

## Code Style
- Prefer Server Components by default, add 'use client' only when needed
- Use `async/await`, never `.then()` callbacks
- Import paths: use @/ alias for src/ directory
- Zod for all external data validation
- drizzle-kit for migrations, never raw SQL migrations

## Patterns
- API routes: /app/api/[route]/route.ts
- Server Actions: colocate with component in actions.ts
- Error handling: use Result type pattern, never throw in Server Components

Continue.dev: Open Source AI Coding Extension

# Install Continue.dev VS Code extension, then optionally use npm packages:
npm install @continuedev/core  # For custom integrations

// Custom context provider for Continue.dev:
// (Add to .continue/config.ts in your project)
import type { IdeSettings, ContextProviderDescription } from '@continuedev/core';

// Custom context provider — adds your docs/codebase context:
export const myContextProvider = {
  title: 'myDocs',
  displayTitle: 'My Library Docs',
  description: 'Documentation for our internal library',
  type: 'query' as const,
  
  async getContextItems(query: string, extras: any) {
    // Fetch relevant docs based on query:
    const docs = await searchDocs(query);
    return docs.map(doc => ({
      name: doc.title,
      description: doc.description,
      content: doc.content,
    }));
  },
};

// .continue/config.json — project-level Continue config:
{
  "models": [
    {
      "title": "Claude Sonnet",
      "provider": "anthropic",
      "model": "claude-sonnet-4-5",
      "apiKey": "$ANTHROPIC_API_KEY"
    },
    {
      "title": "GPT-4o",
      "provider": "openai",
      "model": "gpt-4o",
      "apiKey": "$OPENAI_API_KEY"
    }
  ],
  "contextProviders": [
    { "name": "code" },
    { "name": "docs" },
    { "name": "terminal" },
    { "name": "diff" }
  ],
  "slashCommands": [
    {
      "name": "test",
      "description": "Write unit tests for highlighted code",
      "step": "WriteTestsStep"
    }
  ]
}

MCP: The Real Integration Standard

// Build an MCP server that works with BOTH Cursor and Continue.dev:
// (And Claude Desktop, any MCP-compatible AI tool)

import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js';

const server = new Server(
  { name: 'my-dev-tools', version: '1.0.0' },
  { capabilities: { tools: {} } }
);

// Tool: search your codebase/docs for AI context
server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: 'search_codebase',
      description: 'Search the codebase for relevant code snippets',
      inputSchema: {
        type: 'object',
        properties: {
          query: { type: 'string', description: 'Search query' },
          language: { type: 'string', description: 'Filter by language' },
        },
        required: ['query'],
      },
    },
    {
      name: 'get_component_docs',
      description: 'Get documentation for a specific component',
      inputSchema: {
        type: 'object',
        properties: { componentName: { type: 'string' } },
        required: ['componentName'],
      },
    },
  ],
}));

server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.params.name === 'search_codebase') {
    const results = await searchCodebase(request.params.arguments.query);
    return { content: [{ type: 'text', text: JSON.stringify(results) }] };
  }
  if (request.params.name === 'get_component_docs') {
    const docs = await getComponentDocs(request.params.arguments.componentName);
    return { content: [{ type: 'text', text: docs }] };
  }
  throw new Error('Unknown tool');
});

const transport = new StdioServerTransport();
await server.connect(transport);

// Add to Cursor Settings (claude_desktop_config.json equivalent for Cursor):
{
  "mcpServers": {
    "my-dev-tools": {
      "command": "node",
      "args": ["/path/to/mcp-server.js"]
    }
  }
}

Making npm Packages AI-Friendly

// Best practices for npm package authors in 2026:

// 1. Rich JSDoc — AI tools read these:
/**
 * Creates a new user in the database.
 * @param email - Must be a valid email address
 * @param name - Display name, 2-100 characters
 * @param options.role - Defaults to 'user'. Use 'admin' for full access.
 * @example
 * const user = await createUser('alice@example.com', 'Alice', { role: 'admin' });
 */
export async function createUser(email: string, name: string, options?: CreateUserOptions): Promise<User>

// 2. .cursorrules at package root (for monorepos):
// Uses the package correctly out of the box

// 3. llms.txt at your docs site:
// Structured docs format AI tools can consume
// https://yourlibrary.dev/llms.txt

// 4. TypeScript types — AI autocomplete uses these for suggestions

Decision Guide

Build a Cursor integration if:
  → .cursorrules file for project conventions (free, no code)
  → VS Code extension that works in Cursor (standard extension API)
  → MCP server for providing your tool's data/context to AI

Use Continue.dev if:
  → Want open source, self-hosted AI coding assistant
  → Need custom context providers (internal docs, APIs)
  → Team uses VS Code or JetBrains

Build with @continuedev/core if:
  → Building a custom AI coding tool or extension
  → Need programmatic access to Continue's capabilities

The 2026 AI dev tools stack:
  → Cursor or Continue.dev (the AI IDE layer)
  → MCP servers (your custom context/tools)
  → .cursorrules (project conventions)
  → Good TypeScript types and JSDoc (AI autocomplete)

MCP Server Architecture and Security Considerations

Building an MCP server that provides context to AI coding tools requires careful security design. MCP servers run with the permissions of the process that invokes them — a stdio-based MCP server launched by Cursor or Claude Desktop can read any file the user can read, execute any command, and make any network request. This is by design (MCP servers need broad access to be useful) but means you must trust every MCP server you install. For teams building internal MCP servers that access proprietary codebases or internal APIs, the server should authenticate API calls, respect existing access controls, and avoid caching sensitive data beyond the scope of a single request. When exposing MCP tools that can write to files or execute commands, implement explicit confirmation steps or audit logging so developers can review what the AI triggered on their behalf.

Continue.dev Self-Hosting and Privacy

Continue.dev's open-source architecture is a meaningful advantage for teams with strict data privacy requirements. By running Continue.dev with a self-hosted LLM (via Ollama, vLLM, or a private API gateway), your code never leaves your infrastructure. The @continuedev/core package handles the extension integration, context window management, and model communication — you configure the model endpoint to point to your internal deployment. This matters for industries with regulatory constraints (finance, healthcare, government) where sending source code to third-party APIs may violate compliance requirements. Cursor, by contrast, sends code context to Cursor's cloud infrastructure and then to the underlying model provider (Anthropic or OpenAI) — their data processing agreement is available but the data leaves your systems regardless. For open-source projects without confidentiality requirements, this distinction is irrelevant.

TypeScript and Language Server Integration

Both Cursor and Continue.dev build on top of VS Code's Language Server Protocol (LSP), which means TypeScript's full type information is available as context for AI code generation. Cursor's AI completion reads the TypeScript language server's inferred types for the current cursor position, function signatures from imported modules, and JSDoc comments — this is why AI suggestions for TypeScript code are often more accurate than for untyped JavaScript. Continue.dev's @code context provider similarly pulls LSP-informed context when you reference a function or class. Practical implication: maintaining rich TypeScript types, detailed JSDoc comments, and meaningful interface names directly improves AI suggestion quality in both tools. This creates an interesting incentive: good TypeScript practices that benefit human developers also produce better AI-assisted code.

Custom AI Coding Workflows with @modelcontextprotocol/sdk

The @modelcontextprotocol/sdk package is where the 2026 AI tooling ecosystem is converging. Any tool that speaks MCP can integrate with Cursor, Claude Desktop, Continue.dev, and any future MCP-compatible assistant. This is similar to how LSP created a common language between editors and language servers — MCP is doing the same for AI tool integrations. Practical MCP server use cases for development teams include: serving internal API documentation as context, searching a proprietary package registry for available modules, querying team-specific style guides and patterns, and accessing project-specific configuration that affects code generation. The @modelcontextprotocol/sdk npm package provides TypeScript types, connection handling, and transport abstractions (stdio for local tools, HTTP+SSE for remote servers) so building an MCP server requires only implementing your tool's business logic.

Building AI-Friendly npm Packages

Package authors can take concrete steps to improve how AI coding tools interact with their libraries. Clear TypeScript types are the foundation — well-typed function signatures let AI tools suggest correct usage patterns without needing to understand the implementation. Beyond types, rich JSDoc comments with @param, @returns, @throws, and @example tags give AI tools the documentation they need to generate correct call sites. The @example tag is particularly valuable: Cursor and Continue.dev read examples from JSDoc and use them as few-shot patterns when suggesting code using your library. Publishing an llms.txt file at your documentation site root (following the emerging convention from llmstxt.org) provides AI tools with a structured summary of your library's API designed specifically for LLM consumption. For monorepo packages, placing a focused .cursorrules file in the package directory ensures that developers working in that package get AI suggestions that follow the package's conventions and patterns.

Team Adoption and Workflow Integration

Adopting AI coding tools effectively requires deliberate team practices beyond individual configuration. Sharing .cursorrules files via version control ensures the entire team's AI assistant follows the same conventions — commit the file to the repository root and include it in your project's onboarding documentation. For teams using Continue.dev, commit the .continue/config.json file to share model configuration and context providers across team members. Code review should treat AI-generated code the same as human-written code — review for correctness, security, and alignment with project patterns regardless of how it was produced. Teams that get the most value from AI coding tools are those that invest in writing comprehensive context for the AI: detailed type definitions, thorough JSDoc, rich error messages, and well-named variables all improve suggestion quality beyond what any configuration change can achieve. The quality of AI suggestions reflects the quality of the codebase the AI reads for context.

Context Window and Codebase Indexing

Cursor and Continue.dev both build and maintain a codebase index to answer questions about your code without exceeding the LLM's context window.

Cursor's codebase indexing runs automatically when you open a folder, creating embeddings of your files and updating them incrementally on save. The @codebase command in Cursor's chat mode retrieves the most relevant code snippets for your query from the embedding index. This enables accurate "how does X work in this repo?" questions without manually providing context. Cursor also uses tree-sitter to parse code structure, improving retrieval precision for function-level and class-level queries.

Continue.dev's context providers are more explicit — you add context to queries by prefixing with @file, @folder, @docs, or custom providers you configure. The @codebase provider (requires embedding model configuration) provides semantic search similar to Cursor, but setting it up requires configuring an embedding model endpoint. Continue.dev's approach gives more control over what context is included but requires more manual management compared to Cursor's automatic indexing.

For large monorepos (>100K files), both tools require careful configuration of ignore patterns to prevent indexing generated files, node_modules, and build artifacts that waste context budget without providing useful retrieval signal.

Compare AI coding tool packages on PkgPulse.

See also: Claude Code vs Cursor vs Copilot for JS Devs 2026 and Cursor vs Continue.dev 2026: Which AI Editor?, 20 Fastest-Growing npm Packages in 2026 (Data-Backed).