Optimist MCP Server
# Optimist MCP Server
> An intelligent code optimization MCP server that analyzes and improves codebases across multiple dimensions
[](https://opensource.org/licenses/MIT)
[](https://www.typescriptlang.org/)
[](https://modelcontextprotocol.io)
[](https://github.com/Atomic-Germ/mcp-optimist/actions/workflows/ci.yml)
## Overview
Optimist is a Model Context Protocol (MCP) server designed to work alongside other development tools to provide comprehensive codebase optimization. It analyzes code for performance bottlenecks, memory issues, code smells, and maintainability concerns, offering actionable suggestions for improvement.
### Key Features
- š **Performance Analysis** - Identify bottlenecks and hot paths
- š¾ **Memory Optimization** - Detect leaks and inefficient allocations
- š **Code Quality Metrics** - Complexity analysis and maintainability scoring
- š **Dead Code Detection** - Find and eliminate unused code
- š¦ **Dependency Management** - Optimize and analyze dependency graphs
- šÆ **Smart Refactoring** - AI-powered refactoring suggestions
- š **MCP Integration** - Seamless integration with other MCP tools
- ā
**Test-Driven** - Built using TDD methodology
## Quick Start
### Prerequisites
- Node.js 18+
- npm or pnpm
- An MCP-compatible client (e.g., Claude Desktop)
- A codebase to analyze
### Installation
```bash
# Clone the repository
git clone https://github.com/Atomic-Germ/mcp-optimist.git
cd mcp-optimist
npm install
npm run build
```
### Test the Server
```bash
# Run tests to verify everything works
npm test
# Run with coverage
npm run test:coverage
# Verify build output
ls -la dist/
```
### Configure MCP Client
#### Claude Desktop
Edit your configuration file:
- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`
- **Windows**: `%APPDATA%\Claude\claude_desktop_config.json`
- **Linux**: `~/.config/claude/claude_desktop_config.json`
Add the server:
```json
{
"mcpServers": {
"optimist": {
"command": "node",
"args": ["/absolute/path/to/mcp-optimist/dist/index.js"],
"env": {}
}
}
}
```
### Verify Setup
1. Restart your MCP client
2. Look for "optimist" in the available tools
3. You should see 8 optimization tools available
### First Code Analysis
Try these examples in your MCP client:
#### Analyze Code Complexity
```
Use analyze_complexity tool on your project:
Path: "./src"
Max Complexity: 10
Report Format: "summary"
```
#### Detect Performance Issues
```
Use analyze_performance tool:
Path: "./src"
Include Tests: false
Threshold: "medium"
```
#### Find Code Smells
```
Use detect_code_smells tool:
Path: "./src"
Severity: "medium"
```
#### Memory Analysis
```
Use optimize_memory tool:
Path: "./src"
Detect Leaks: true
Suggest Fixes: true
```
## Development
### Development Commands
```bash
# Development
npm run dev # Run with ts-node (development mode)
npm run build:watch # Auto-rebuild on changes
# Testing
npm test # Run all tests
npm run test:watch # Watch mode for tests
npm run test:coverage # Generate coverage report
# Code Quality
npm run lint # Check code with ESLint
npm run lint:fix # Auto-fix linting issues
npm run format # Format code with Prettier
npm run format:check # Check formatting
# Build
npm run build # Compile to dist/
npm run clean # Remove dist/
```
### Project Structure
```
mcp-optimist/
āāā src/
ā āāā index.ts # MCP server entry point
ā āāā server.ts # OptimistServer class
ā āāā types/ # TypeScript definitions
ā āāā tools/ # Tool implementations
ā āāā analyzers/ # Analysis engines
ā āāā utils/ # Utility functions
ā
āāā tests/
ā āāā unit/ # Unit tests
ā āāā integration/ # Integration tests
ā āāā fixtures/ # Test fixtures
ā
āāā docs/ # Documentation
āāā archive/ # Archived documentation
āāā README.md # This file
āāā package.json # Dependencies and scripts
āāā tsconfig.json # TypeScript configuration
āāā jest.config.js # Test configuration
āāā eslint.config.js # Linting rules
āāā .prettierrc # Code formatting
āāā dist/ # Compiled JavaScript
```
## Examples
### Basic Project Analysis
Perform comprehensive analysis of your entire project:
```typescript
// Analyze overall code quality
{
tool: "detect_code_smells",
arguments: {
path: "./src",
severity: "medium"
}
}
// Check performance issues
{
tool: "analyze_performance",
arguments: {
path: "./src",
threshold: "medium",
includeTests: false
}
}
// Find complexity issues
{
tool: "analyze_complexity",
arguments: {
path: "./src",
maxComplexity: 8,
reportFormat: "detailed"
}
}
```
### Single File Analysis
Analyze a specific problematic file:
```typescript
{
tool: "analyze_performance",
arguments: {
path: "./src/services/dataProcessor.ts",
threshold: "low",
profileHotPaths: true,
trackAsyncOperations: true
}
}
```
### Memory Optimization
Find and fix memory leaks in a React component:
```typescript
{
tool: "optimize_memory",
arguments: {
path: "./src/components",
detectLeaks: true,
analyzeClosures: true
}
}
```
**Leak Analysis Result:**
```typescript
{
data: {
findings: [
{
type: 'event-listener-leak',
file: 'src/components/DataChart.tsx',
line: 23,
description: 'Event listeners not cleaned up in useEffect',
leakPotential: 'high',
},
{
type: 'closure-retention',
file: 'src/hooks/useDataFetch.ts',
line: 15,
description: 'Closure retaining large objects unnecessarily',
},
];
}
}
```
**Memory Leak Fixes:**
Problem - Event Listener Leak:
```typescript
// Problematic - no cleanup
function DataChart() {
useEffect(() => {
window.addEventListener('resize', handleResize);
// Missing cleanup function
}, []);
}
```
Fixed:
```typescript
// Fixed with proper cleanup
function DataChart() {
useEffect(() => {
const handleResize = () => {
// Handle resize
};
window.addEventListener('resize', handleResize);
// Cleanup function prevents leak
return () => {
window.removeEventListener('resize', handleResize);
};
}, []);
}
```
### Performance Optimization
Identify and fix performance bottlenecks:
```typescript
{
tool: "analyze_performance",
arguments: {
path: "./src/services/dataProcessor.ts",
threshold: "low",
profileHotPaths: true
}
}
```
**Before (Problematic):**
```typescript
// O(n²) complexity - problematic
function processLargeDataset(items: Item[], lookup: LookupItem[]): ProcessedItem[] {
return items.map((item) => {
// Inner loop for each item - O(n²)
const match = lookup.find((l) => l.id === item.lookupId);
return { ...item, enrichedData: match?.data };
});
}
```
**After (Optimized):**
```typescript
// O(n) complexity - optimized
function processLargeDataset(items: Item[], lookup: LookupItem[]): ProcessedItem[] {
// Create lookup map once - O(n)
const lookupMap = new Map(lookup.map((l) => [l.id, l.data]));
// Single pass through items - O(n)
return items.map((item) => ({
...item,
enrichedData: lookupMap.get(item.lookupId),
}));
}
```
### Code Quality Analysis
Analyze function complexity and code smells:
```typescript
{
tool: "analyze_complexity",
arguments: {
path: "./src/utils/validation.ts",
maxComplexity: 6,
includeCognitive: true
}
}
{
tool: "detect_code_smells",
arguments: {
path: "./src/services/UserService.ts",
severity: "high"
}
}
```
---
For more examples, see the [API Reference](docs/API_REFERENCE.md).
TDQS
Scored across 8 tools
Most tools target clearly distinct concerns: complexity, code smells, dead code, dependencies, memory, and refactoring. The only point of confusion is between analyze_performance and optimize_hot_paths, both of which deal with performance bottlenecks and could be misselected by an agent.
All tool names follow a consistent snake_case verb_noun pattern using clear action verbs (analyze, optimize, detect, find, suggest). The convention is uniform and predictable, making it easy to infer the purpose of each tool from its name.
With 8 tools, the server is well-scoped for a code analysis and optimization domain. Each tool covers a distinct aspect of the problem space without unnecessary bloat or redundancy.
The tool surface covers the major dimensions of code quality: performance, memory, complexity, smells, dead code, dependencies, and refactoring. A minor gap is the lack of a tool to directly apply or validate changes, but the server's analytical focus is well served by the existing set.