Two protocols are shaping modern development tools: Language Server Protocol (LSP) for IDE intelligence, and Model Context Protocol (MCP) for AI integration. Understanding both helps you leverage the full power of AI-assisted development.
What These Protocols Do#
LSP: Making IDEs Smart#
Language Server Protocol standardizes how IDEs get language features:
Before LSP:
- Each IDE implements each language separately
- N IDEs × M languages = N×M implementations
- Inconsistent features across tools
After LSP:
- One language server per language
- Any IDE connects to any server
- N + M implementations
- Consistent features everywhere
LSP provides:
- Autocomplete
- Go to definition
- Find references
- Diagnostics (errors, warnings)
- Refactoring
- Hover information
MCP: Making AI Context-Aware#
Model Context Protocol standardizes how AI accesses your development environment:
Before MCP:
- AI sees only current file
- No database access
- No API understanding
- No tool integration
- Limited context
After MCP:
- AI sees full codebase
- Direct database queries
- API contract understanding
- CI/CD integration
- Complete context
MCP provides:
- Codebase context
- Database access
- API integration
- External tool connections
- Persistent memory
Protocol Comparison#
| Aspect | LSP | MCP |
|---|---|---|
| Purpose | IDE features | AI context |
| Era | 2016+ | 2024+ |
| Connection | IDE ↔ Language Server | AI ↔ Development Tools |
| Scope | Single language | Entire stack |
| Real-time | Yes | Yes |
| Standard | Widely adopted | Growing adoption |
How They Work#
LSP Architecture#
┌─────────────┐ ┌──────────────────┐
│ VS Code │ ◄─────► │ TypeScript LSP │
│ │ JSON │ │
│ Cursor │ RPC │ Rust Analyzer │
│ │ │ │
│ Neovim │ ◄─────► │ Python LSP │
└─────────────┘ └──────────────────┘
IDE Language Servers
Example LSP request:
1{
2 "method": "textDocument/completion",
3 "params": {
4 "textDocument": { "uri": "file:///src/app.ts" },
5 "position": { "line": 10, "character": 15 }
6 }
7}Example LSP response:
1{
2 "items": [
3 { "label": "useState", "kind": "Function" },
4 { "label": "useEffect", "kind": "Function" },
5 { "label": "useCallback", "kind": "Function" }
6 ]
7}MCP Architecture#
┌─────────────┐ ┌──────────────────┐
│ Claude │ │ Database MCP │
│ │ ◄─────► │ Server │
│ Bootspring│ MCP │ │
│ │ │ GitHub MCP │
│ Custom AI │ ◄─────► │ Server │
└─────────────┘ │ │
AI │ Filesystem MCP │
│ Server │
└──────────────────┘
Context Providers
Example MCP request:
1{
2 "method": "tools/call",
3 "params": {
4 "name": "query_database",
5 "arguments": {
6 "query": "SELECT * FROM users WHERE active = true"
7 }
8 }
9}Example MCP response:
1{
2 "content": [
3 {
4 "type": "text",
5 "text": "Found 1,234 active users. Schema: id, email, name, created_at..."
6 }
7 ]
8}Complementary, Not Competing#
LSP and MCP serve different purposes and work together:
LSP Handles:#
- Syntax highlighting
- Error checking
- Code navigation
- Refactoring
- Type checking
MCP Handles:#
- AI understanding your project
- Database schema awareness
- API contract knowledge
- External service integration
- Team context sharing
Together They Enable:#
1// LSP provides:
2// - TypeScript type checking
3// - Autocomplete from type definitions
4// - Error highlighting
5
6// MCP provides:
7// - AI understanding you're building a user dashboard
8// - Knowledge that User has 15 fields in the database
9// - Awareness of the API endpoints available
10// - History of similar components in your codebase
11
12// Combined result:
13// AI suggests complete, context-aware implementations
14// that type-check correctly and follow your patternsPractical Example#
Without These Protocols#
Developer asks AI: "Add a function to get user orders"
AI responds with generic code:
function getUserOrders(userId: string) {
// Generic implementation
return fetch(`/api/orders?user=${userId}`)
.then(res => res.json());
}With LSP Only#
AI gets type information:
function getUserOrders(userId: string): Promise<Order[]> {
// Types are correct
return fetch(`/api/orders?user=${userId}`)
.then(res => res.json() as Order[]);
}With LSP + MCP#
AI understands your full context:
1// AI knows:
2// - Your API is at /api/v2/
3// - Orders have pagination
4// - You use axios, not fetch
5// - Your error handling pattern
6// - The database has an index on user_id
7
8async function getUserOrders(
9 userId: string,
10 page = 1,
11 limit = 20
12): Promise<PaginatedResponse<Order>> {
13 try {
14 const response = await api.get('/api/v2/orders', {
15 params: { userId, page, limit }
16 });
17 return response.data;
18 } catch (error) {
19 logger.error('Failed to fetch orders', { userId, error });
20 throw new OrderFetchError(userId, error);
21 }
22}MCP Server Types#
Database Servers#
1{
2 "database": {
3 "type": "postgresql",
4 "capabilities": [
5 "query",
6 "schema_introspection",
7 "explain_analyze"
8 ]
9 }
10}Enables:
- AI understanding table relationships
- Query optimization suggestions
- Schema-aware code generation
Version Control Servers#
1{
2 "github": {
3 "capabilities": [
4 "read_files",
5 "list_commits",
6 "read_issues",
7 "read_prs"
8 ]
9 }
10}Enables:
- AI understanding code history
- PR context awareness
- Issue-to-code mapping
Infrastructure Servers#
1{
2 "kubernetes": {
3 "capabilities": [
4 "list_deployments",
5 "read_configs",
6 "check_status"
7 ]
8 }
9}Enables:
- AI understanding deployment context
- Infrastructure-aware suggestions
- DevOps automation
Building with Both Protocols#
For Tool Builders#
If you're building developer tools:
Use LSP for:
- Language-specific features
- Real-time code analysis
- IDE integration
Use MCP for:
- AI enhancement
- Context sharing
- Tool integration
For Developers#
Leverage LSP:
- Install language servers for your languages
- Use LSP-aware editors (VS Code, Neovim)
- Get consistent features everywhere
Leverage MCP:
- Use MCP-enabled AI tools (Bootspring, Claude)
- Connect your databases and APIs
- Enable full-stack AI understanding
The Future#
LSP Evolution#
- Better refactoring support
- Improved cross-file understanding
- Language-agnostic features
MCP Evolution#
- More server types
- Deeper integrations
- Standardized authentication
- Enterprise features
Convergence#
Eventually, expect:
- AI tools that use LSP for language understanding
- LSP servers enhanced with AI capabilities
- Unified protocols for complete IDE intelligence
Choosing Your Tools#
If You Need Language Features:#
LSP is mature and widely supported:
- TypeScript: typescript-language-server
- Python: Pylsp or Pyright
- Rust: rust-analyzer
- Go: gopls
If You Need AI Context:#
MCP is newer but powerful:
- Bootspring: Full MCP platform
- Claude: Native MCP support
- Custom servers: Build your own
For Maximum Power:#
Use both:
11. IDE with LSP support (VS Code, Cursor)
2 - Get language intelligence
3
42. MCP-enabled AI (Bootspring)
5 - Get full context awareness
6
73. Result: AI that understands both your
8 language AND your specific projectConclusion#
LSP and MCP represent two generations of developer tooling:
- LSP (2016): Made IDEs consistently smart about languages
- MCP (2024): Making AI consistently smart about your context
Together, they enable AI assistants that understand both the language you're writing and the project you're building.
The best tools leverage both.
Bootspring is built on MCP, giving AI deep understanding of your entire development stack. Try it free and experience the difference context makes.