Content
# Code Executor MCP
**Stop hitting the 2-3 MCP server wall.** One MCP to orchestrate them all - 98% token savings, unlimited tool access.
[](https://www.npmjs.com/package/code-executor-mcp)
[](https://hub.docker.com/r/aberemia24/code-executor-mcp)
[](https://opensource.org/licenses/MIT)
## Why Use Code Executor MCP?
- **98% Token Reduction** - 141k → 1.6k tokens (load 1 executor vs 50+ tools)
- **Sandboxed Security** - Isolated Deno/Python execution, no internet by default, audit logging
- **Type-Safe Wrappers** - Auto-generated TypeScript/Python SDK with full IntelliSense
- **Progressive Disclosure** - Tools loaded on-demand inside sandbox, not upfront in context
- **Zero Config Setup** - Wizard auto-detects existing MCP servers from Claude Code/Cursor
- **Production Ready** - 606 tests, 95%+ coverage, Docker support, rate limiting
## The Problem
You can't use more than 2-3 MCP servers before context exhaustion kills you.
- **Research confirms:** [Tool accuracy drops significantly after 2-3 servers](https://www.mcpjam.com/blog/claude-agent-skills)
- **6,490+ MCP servers available**, but you can only use 2-3
- **47 tools = 141k tokens** consumed before you write a single word
**You're forced to choose:** filesystem OR browser OR git OR AI tools. Never all of them.
## The Solution
Disable all MCPs. Enable only `code-executor-mcp`.
```bash
# Before: 47 tools, 141k tokens
mcp__filesystem__read_file
mcp__filesystem__write_file
mcp__git__commit
mcp__browser__navigate
... 43 more tools
# After: 2 tools, 1.6k tokens (98% reduction)
run-typescript-code
run-python-code
```
**Inside the sandbox**, access ANY MCP tool on-demand:
```typescript
// Claude writes this automatically
const file = await callMCPTool('mcp__filesystem__read_file', { path: '/src/app.ts' });
const review = await callMCPTool('mcp__zen__codereview', { code: file });
await callMCPTool('mcp__git__commit', { message: review.suggestions });
```
**Result:** Unlimited MCP access, zero context overhead.
### How Progressive Disclosure Works
```mermaid
sequenceDiagram
participant C as Claude/Cursor
participant E as Code Executor
participant M as Other MCPs
Note over C: ❌ Traditional: Load 50+ tools (141k tokens)
Note over C: ✅ Code Executor: Load 2 tools (1.6k tokens)
C->>E: run-typescript-code
rect rgb(240, 248, 255)
Note right of E: Sandbox (on-demand discovery)
E->>M: callMCPTool('mcp__filesystem__read_file')
M-->>E: Return data
end
E-->>C: Return result
```
Traditional MCP exposes all 47 tools upfront (141k tokens). Code Executor exposes 2 tools with outputSchema (1.6k tokens), loading others on-demand inside the sandbox when needed.
## Quick Start
### Option 1: Interactive Setup Wizard (Recommended)
Don't configure manually. Our wizard does everything:
```bash
npm install -g code-executor-mcp
code-executor-mcp setup
```
**What the wizard does:**
1. 🔍 Scans for existing MCP configs (Claude Code `~/.claude.json`, Cursor `~/.cursor/mcp.json`, project `.mcp.json`)
2. ⚙️ Configures with smart defaults (or customize interactively)
3. 🤖 **NEW**: Writes complete MCP configuration (sampling + security + sandbox + performance)
4. 📦 Generates type-safe TypeScript/Python wrappers for autocomplete
5. 📅 Optional: Sets up daily sync to keep wrappers updated
**Complete Configuration** (all written automatically):
- **AI Sampling**: Multi-provider support (Anthropic, OpenAI, Gemini, Grok, Perplexity)
- **Security**: Audit logging, content filtering, project restrictions
- **Sandbox**: Deno/Python execution with timeouts
- **Performance**: Rate limiting, schema caching, execution timeouts
**Smart defaults** (just press Enter):
- Port: 3333 | Timeout: 120s | Rate limit: 60/min
- Audit logs: `~/.code-executor/audit-logs/`
- Sampling: Disabled (enable optionally with API key)
**Supported AI Tools:** Claude Code and Cursor (more coming soon)
**First-Run Detection:**
If you try to run `code-executor-mcp` without configuration:
```bash
❌ No MCP configuration found
📝 To configure code-executor-mcp, run:
code-executor-mcp setup
Configuration will be created at: ~/.claude.json
```
#### What are Wrappers?
The wizard generates TypeScript/Python wrapper functions for your MCP tools:
**Before** (manual):
```typescript
const file = await callMCPTool('mcp__filesystem__read_file', {
path: '/src/app.ts'
});
```
**After** (wrapper):
```typescript
import { filesystem } from './mcp-wrappers';
const file = await filesystem.readFile({ path: '/src/app.ts' });
```
**Benefits:**
- ✅ Type-safe with full IntelliSense/autocomplete
- ✅ Self-documenting JSDoc comments from schemas
- ✅ No need to remember exact tool names
- ✅ Matches actual MCP tool APIs (generated from schemas)
**Keeping Wrappers Updated:**
The wizard can set up daily sync (optional) to automatically regenerate wrappers:
- **macOS**: launchd plist runs at 4-6 AM
- **Linux**: systemd timer runs at 4-6 AM
- **Windows**: Task Scheduler runs at 4-6 AM
Daily sync re-scans your AI tool configs and project config for new/removed MCP servers. You can also manually update anytime with `code-executor-mcp setup`.
### Option 2: Manual Configuration
#### 1. Install
```bash
npm install -g code-executor-mcp
```
#### 2. Configure
**IMPORTANT:** Code-executor discovers and merges MCP servers from BOTH locations:
- **Global:** `~/.claude.json` (cross-project MCPs like voice-mode, personal tools)
- **Project:** `.mcp.json` (team-shared MCPs in your project root)
**Config Merging:** Global MCPs + Project MCPs = All available (project overrides global for duplicate names)
Add to your **project** `.mcp.json` or **global** `~/.claude.json`:
```json
{
"mcpServers": {
"code-executor": {
"command": "npx",
"args": ["-y", "code-executor-mcp"],
"env": {
"MCP_CONFIG_PATH": "/full/path/to/this/.mcp.json",
"DENO_PATH": "/path/to/.deno/bin/deno"
}
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user"]
},
"playwright": {
"command": "npx",
"args": ["-y", "@playwright/mcp", "--headless"]
}
}
}
```
**Configuration Guide:**
- `MCP_CONFIG_PATH`: Optional - points to project `.mcp.json` (still discovers global `~/.claude.json`)
- `DENO_PATH`: Run `which deno` to find it (required for TypeScript execution)
- **Global MCPs** (`~/.claude.json`): Personal servers available across all projects
- **Project MCPs** (`.mcp.json`): Team-shared servers in version control
- **Connection Flow**: Claude Code → code-executor ONLY, then code-executor → all other MCPs
**Quick Setup:**
```bash
# Find Deno path
which deno
# Output: /home/user/.deno/bin/deno
# Project config (team-shared)
realpath .mcp.json
# Output: /home/user/projects/myproject/.mcp.json
# Global config (personal)
ls ~/.claude.json
# Output: /home/user/.claude.json
# Code-executor automatically merges both!
```
**Minimal (Python-only):**
```json
{
"mcpServers": {
"code-executor": {
"command": "npx",
"args": ["-y", "code-executor-mcp"],
"env": {
"MCP_CONFIG_PATH": "/path/to/.mcp.json",
"PYTHON_ENABLED": "true"
}
}
}
}
```
#### 3. Use
Claude can now access any MCP tool through code execution:
```typescript
// Claude writes this when you ask to "read package.json"
const result = await callMCPTool('mcp__filesystem__read_file', {
path: './package.json'
});
console.log(result);
```
That's it. No configuration, no allowlists, no manual tool setup.
## Real-World Example
**Task:** "Review auth.ts for security issues and commit fixes"
**Without code-executor** (impossible - hit context limit):
```
Can't enable: filesystem + git + zen codereview
Pick 2, manually do the 3rd
```
**With code-executor** (single AI message):
```typescript
// Read file
const code = await callMCPTool('mcp__filesystem__read_file', {
path: '/src/auth.ts'
});
// Review with AI
const review = await callMCPTool('mcp__zen__codereview', {
step: 'Security audit',
code: code,
step_number: 1,
total_steps: 1
});
// Apply fixes
const fixed = review.suggestions.replace(/timing-attack/g, 'constant-time');
await callMCPTool('mcp__filesystem__write_file', {
path: '/src/auth.ts',
content: fixed
});
// Commit
await callMCPTool('mcp__git__commit', {
message: 'fix: constant-time token comparison'
});
console.log('Security fixes applied and committed');
```
**All in ONE tool call.** Variables persist, no context switching.
## Features
| Feature | Description |
|---------|-------------|
| **98% Token Savings** | 141k → 1.6k tokens (47 tools → 2 tools) |
| **Unlimited MCPs** | Access 6,490+ MCP servers without context limits |
| **Multi-Step Workflows** | Chain multiple MCP calls in one execution |
| **Auto-Discovery** | AI agents find tools on-demand (0 token cost) |
| **Deep Validation** | AJV schema validation with helpful error messages |
| **Security** | Sandboxed (Deno/Python), allowlists, audit logs, rate limiting |
| **Production Ready** | TypeScript, 606 tests, 95%+ coverage, Docker support |
## MCP Sampling (Beta) - LLM-in-the-Loop Execution
**New in v1.0.0:** Enable Claude to call itself during code execution for dynamic reasoning and analysis.
### What is Sampling?
MCP Sampling allows TypeScript and Python code running in sandboxed environments to invoke Claude (via Anthropic's API) through a simple interface. Your code can now "ask Claude for help" mid-execution.
**Use Cases:**
- **Code Analysis**: Read a file, ask Claude to analyze it for security issues
- **Multi-Step Reasoning**: Have Claude break down complex tasks into steps
- **Data Processing**: Process each file/record with Claude's intelligence
- **Interactive Debugging**: Ask Claude to explain errors or suggest fixes
### Quick Example
**TypeScript:**
```typescript
// Enable sampling in your execution
const result = await callMCPTool('mcp__code-executor__executeTypescript', {
code: `
// Read a file
const code = await callMCPTool('mcp__filesystem__read_file', {
path: './auth.ts'
});
// Ask Claude to analyze it
const analysis = await llm.ask(
'Analyze this code for security vulnerabilities: ' + code
);
console.log(analysis);
`,
enableSampling: true, // Enable sampling
allowedTools: ['mcp__filesystem__read_file']
});
// Check sampling metrics
console.log('Rounds:', result.samplingMetrics.totalRounds);
console.log('Tokens:', result.samplingMetrics.totalTokens);
```
**Python:**
```python
# Python example with sampling
code = """
import json
# Read data
data = call_mcp_tool('mcp__filesystem__read_file', {'path': './data.json'})
# Ask Claude to summarize
summary = await llm.ask(f'Summarize this data: {data}')
print(summary)
"""
result = call_mcp_tool('mcp__code-executor__executePython', {
'code': code,
'enableSampling': True
})
```
### API Reference
**TypeScript API:**
- `llm.ask(prompt: string, options?)` - Simple query, returns response text
- `llm.think({messages, model?, maxTokens?, systemPrompt?})` - Multi-turn conversation
**Python API:**
- `llm.ask(prompt: str, system_prompt='', max_tokens=1000)` - Simple query
- `llm.think(messages, model='', max_tokens=1000, system_prompt='')` - Multi-turn conversation
### Security Controls
Sampling includes enterprise-grade security controls:
| Control | Description |
|---------|-------------|
| **Rate Limiting** | Max 10 rounds, 10,000 tokens per execution (configurable) |
| **Content Filtering** | Auto-redacts secrets (API keys, tokens) and PII (emails, SSNs) |
| **System Prompt Allowlist** | Only pre-approved prompts accepted (prevents prompt injection) |
| **Bearer Token Auth** | 256-bit secure token per bridge session |
| **Localhost Binding** | Bridge server only accessible locally (no external access) |
| **Audit Logging** | All calls logged with SHA-256 hashes (no plaintext secrets) |
### Configuration
**Enable Sampling:**
Option 1 - Per-Execution (recommended):
```typescript
{ enableSampling: true }
```
Option 2 - Environment Variable:
```bash
export CODE_EXECUTOR_SAMPLING_ENABLED=true
export CODE_EXECUTOR_MAX_SAMPLING_ROUNDS=10
export CODE_EXECUTOR_MAX_SAMPLING_TOKENS=10000
```
Option 3 - Config File (`~/.code-executor/config.json`):
```json
{
"sampling": {
"enabled": true,
"maxRoundsPerExecution": 10,
"maxTokensPerExecution": 10000,
"allowedSystemPrompts": [
"",
"You are a helpful assistant",
"You are a code analysis expert"
]
}
}
```
### Hybrid Architecture
Code Executor automatically detects the best sampling method:
1. **MCP SDK Sampling** (free) - If your MCP client supports `sampling/createMessage`
2. **Direct Anthropic API** (paid) - Fallback if MCP sampling unavailable (requires `ANTHROPIC_API_KEY`)
**⚠️ Claude Code Limitation (as of November 2025)**:
Claude Code does **not** support MCP sampling yet ([Issue #1785](https://github.com/anthropics/claude-code/issues/1785)). When using Claude Code, sampling will fall back to Direct API mode (requires `ANTHROPIC_API_KEY`).
**Compatible clients with MCP sampling**:
- ✅ VS Code (v0.20.0+)
- ✅ GitHub Copilot
- ❌ Claude Code (pending Issue #1785)
When Claude Code adds sampling support, no code changes are needed - it will automatically switch to free MCP sampling.
### Documentation
See the comprehensive sampling guide: [docs/sampling.md](docs/sampling.md)
**Covers:**
- What/Why/How with architecture diagrams
- Complete API reference for TypeScript & Python
- Security model with threat matrix
- Configuration guide (env vars, config file, per-execution)
- Troubleshooting guide (8 common errors)
- Performance benchmarks (<50ms bridge startup)
- FAQ (15+ questions)
## Security (Enterprise-Grade)
Code Executor doesn't just "run code." It secures it:
| Feature | Implementation |
|---------|---------------|
| **Sandbox Isolation** | Deno (TypeScript) with restricted permissions, Pyodide WebAssembly (Python) |
| **File Access Control** | Non-root user (UID 1001), read-only root FS, explicit project path allowlist |
| **Network Policy** | NO internet by default, explicit domain allowlist required |
| **Path Validation** | Symlink resolution, directory traversal protection |
| **Audit Logging** | Every execution logged to `~/.code-executor/audit.jsonl` with timestamps |
| **Rate Limiting** | 30 requests/min default (configurable per MCP) |
| **Dangerous Pattern Detection** | Blocks eval, exec, __import__, pickle.loads |
| **Schema Validation** | AJV deep validation before execution |
| **SSRF Protection** | Blocks AWS metadata, localhost, private IPs |
**Example**: Block all internet except GitHub API:
```json
{
"security": {
"allowedDomains": ["api.github.com"],
"allowedProjects": ["/home/user/projects"]
}
}
```
**Sandbox Architecture:**
- **Deno (TypeScript)**: V8 isolate, explicit permissions (--allow-read=/tmp), no shell access
- **Pyodide (Python)**: WebAssembly sandbox, virtual filesystem, network restricted to MCP proxy only
- **Docker (Optional)**: Non-root container, read-only root, minimal attack surface
See [SECURITY.md](SECURITY.md) for complete threat analysis and security model.
## Advanced Usage
### Allowlists (Optional Security)
Restrict which tools can be executed:
```typescript
await callMCPTool('mcp__code-executor__run-typescript-code', {
code: `
// This works
await callMCPTool('mcp__filesystem__read_file', {...});
// This fails - not in allowlist
await callMCPTool('mcp__git__push', {...});
`,
allowedTools: ['mcp__filesystem__read_file']
});
```
### Discovery Functions
AI agents can explore available tools:
```typescript
// Find all tools
const tools = await discoverMCPTools();
// Search for specific functionality
const fileTools = await searchTools('file read write');
// Inspect schema
const schema = await getToolSchema('mcp__filesystem__read_file');
```
**Zero token cost** - discovery functions hidden from AI agent's tool list.
### MCP Sampling: LLM-in-the-Loop Execution
Enable AI to autonomously call other AIs inside sandboxed code for iterative problem-solving, multi-agent collaboration, and complex workflows.
**Key Features:**
- **Multi-Provider Support**: Anthropic, OpenAI, Gemini, Grok, Perplexity
- **Hybrid Mode**: Free MCP sampling with automatic fallback to paid API
- **Simple API**: `llm.ask(prompt)` and `llm.think(messages)` helpers
- **Security**: Rate limiting, content filtering, localhost-only bridge
**Setup:**
```bash
# 1. Create .env file
cp .env.example .env
# 2. Add API key
echo "CODE_EXECUTOR_SAMPLING_ENABLED=true" >> .env
echo "CODE_EXECUTOR_AI_PROVIDER=gemini" >> .env
echo "GEMINI_API_KEY=your_key_here" >> .env
# 3. Use wrapper script (loads .env before starting)
# Update .mcp.json:
{
"code-executor": {
"command": "/path/to/start-with-env.sh"
}
}
```
See [`SAMPLING_SETUP.md`](./SAMPLING_SETUP.md) for complete setup guide.
**Basic Usage:**
```typescript
// Simple question
const answer = await llm.ask('What is 2+2?');
console.log(answer); // "4"
// Multi-turn reasoning
const analysis = await llm.think([
{ role: 'system', content: 'You are a code reviewer' },
{ role: 'user', content: 'Review this code: ...' }
]);
```
**Advanced Example - Multi-Agent Code Review:**
5 AI agents collaborate to review, secure, refactor, test, and document code:
```typescript
// Agent 1: Code Reviewer
const review = await llm.ask('Review this code and list 5 issues...');
// Agent 2: Security Analyst
const security = await llm.ask('Analyze for vulnerabilities...');
// Agent 3: Refactoring Expert
const refactored = await llm.ask('Refactor using ES6+...');
// Agent 4: Test Generator
const tests = await llm.ask('Generate 3 Vitest test cases...');
// Agent 5: Documentation Writer
const docs = await llm.ask('Write JSDoc comments...');
```
**Real-World Results:**
- 5 AI agents, 10 seconds, ~2,600 tokens
- Complete code transformation: review → secure → refactor → test → document
- See [`examples/multi-agent-code-review.ts`](./examples/multi-agent-code-review.ts) for full working example
**Use Cases:**
- 🤖 Multi-agent systems (code review, planning, execution)
- 🔄 Iterative refinement (generate → validate → improve loop)
- 🧪 Autonomous testing (generate tests, run them, fix failures)
- 📚 Auto-documentation (analyze code, write docs, validate examples)
### Multi-Action Workflows
Complex automation in a single tool call:
```typescript
// Launch browser → navigate → interact → extract
await callMCPTool('mcp__code-executor__run-typescript-code', {
code: `
await callMCPTool('mcp__playwright__launch', { headless: false });
await callMCPTool('mcp__playwright__navigate', { url: 'https://example.com' });
const title = await callMCPTool('mcp__playwright__evaluate', {
script: 'document.title'
});
console.log('Page title:', title);
`,
allowedTools: ['mcp__playwright__*']
});
```
State persists across calls - no context switching.
### Python Execution (Pyodide WebAssembly)
Secure Python execution with Pyodide sandbox:
**Enable Python:**
```bash
# Set environment variable (REQUIRED)
export PYTHON_SANDBOX_READY=true
# Enable in config
# .code-executor.json
{
"executors": {
"python": {
"enabled": true
}
}
}
```
**Example - Python with MCP tools:**
```python
import asyncio
async def main():
# Discover available tools
tools = await discover_mcp_tools()
print(f'Found {len(tools)} tools')
# Call MCP tool to read file
content = await call_mcp_tool('mcp__filesystem__read_file', {
'path': '/tmp/data.json'
})
print(f'File content: {content}')
# Process data
import json
data = json.loads(content)
result = [x * 2 for x in data['numbers']]
print(f'Processed: {result}')
asyncio.run(main())
```
**Security guarantees:**
- ✅ WebAssembly sandbox (same security as Deno)
- ✅ Virtual filesystem (no host file access)
- ✅ Network restricted to authenticated MCP proxy
- ✅ No subprocess spawning
- ✅ Memory limited by V8 heap
**Limitations:**
- Pure Python only (no native C extensions unless WASM-compiled)
- ~2-3s first load (Pyodide npm package), <100ms cached
- No multiprocessing/threading (use async/await)
- 10-30% slower than native Python (WASM overhead acceptable for security)
See [SECURITY.md](SECURITY.md#-python-executor-security-pyodide) for complete security model.
## Installation Options
### npm (Recommended)
```bash
npm install -g code-executor-mcp
code-executor-mcp
```
### Docker (Production)
**Quick Start:**
```bash
docker pull aberemia24/code-executor-mcp:latest
docker run -p 3333:3333 aberemia24/code-executor-mcp:latest
```
**With docker-compose (Recommended):**
```bash
# 1. Copy example configuration
cp docker-compose.example.yml docker-compose.yml
# 2. Edit docker-compose.yml to add your API keys (optional)
# - Set CODE_EXECUTOR_SAMPLING_ENABLED="true"
# - Set your provider: CODE_EXECUTOR_AI_PROVIDER="gemini"
# - Add API key: GEMINI_API_KEY="your-key-here"
# 3. Start the service
docker-compose up -d
# 4. View logs
docker-compose logs -f
```
**First-Run Auto-Configuration:**
Docker deployment automatically generates complete MCP configuration from environment variables on first run:
- ✅ All environment variables → comprehensive config
- ✅ Includes sampling, security, sandbox, and performance settings
- ✅ Config saved to `/app/config/.mcp.json`
- ✅ Persistent across container restarts (use volume mount)
See [DOCKER_TESTING.md](DOCKER_TESTING.md) for security details and [docker-compose.example.yml](docker-compose.example.yml) for all available configuration options.
### Local Development
```bash
git clone https://github.com/aberemia24/code-executor-MCP.git
cd code-executor-mcp
npm install && npm run build
npm run server
```
## Configuration
**Complete Example** (`.mcp.json`):
```json
{
"mcpServers": {
"code-executor": {
"command": "npx",
"args": ["-y", "code-executor-mcp"],
"env": {
"MCP_CONFIG_PATH": "/absolute/path/to/.mcp.json",
"DENO_PATH": "/home/user/.deno/bin/deno",
"ENABLE_AUDIT_LOG": "true",
"AUDIT_LOG_PATH": "/home/user/.code-executor/audit.log",
"ALLOWED_PROJECTS": "/home/user/projects:/tmp"
}
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user"]
},
"zen": {
"command": "uvx",
"args": ["--from", "git+https://github.com/zen-mcp.git", "zen-mcp-server"],
"env": {
"GEMINI_API_KEY": "your-key-here"
}
}
}
}
```
**Environment Variables:**
| Variable | Required | Description | Example |
|----------|----------|-------------|---------|
| `MCP_CONFIG_PATH` | ⚠️ Optional | Explicit path to project `.mcp.json` | `/home/user/projects/myproject/.mcp.json` |
| `DENO_PATH` | ✅ For TypeScript | Path to Deno binary | `/home/user/.deno/bin/deno` |
| `ENABLE_AUDIT_LOG` | ⚠️ Recommended | Enable security audit logging | `true` |
| `AUDIT_LOG_PATH` | No | Custom audit log location | `/var/log/code-executor/audit.log` |
| `ALLOWED_PROJECTS` | ⚠️ Recommended | Restrict file access | `/home/user/projects:/tmp` |
| `PYTHON_ENABLED` | No | Enable Python executor | `true` (default) |
**Security Note:** Store API keys in environment variables, not directly in config files.
### Multi-Provider AI Sampling Configuration
**NEW:** Support for 5 AI providers (Anthropic, OpenAI, Gemini, Grok, Perplexity) with automatic provider-specific model selection.
**Quick Setup:**
```bash
# 1. Copy example config
cp .env.example .env
# 2. Edit .env and add your API key
CODE_EXECUTOR_SAMPLING_ENABLED=true
CODE_EXECUTOR_AI_PROVIDER=gemini # cheapest option!
GEMINI_API_KEY=your-key-here
# 3. Start server
npm start
```
**Provider Comparison (January 2025):**
| Provider | Default Model | Cost (Input/Output per MTok) | Best For |
|----------|---------------|------------------------------|----------|
| **Gemini** ⭐ | `gemini-2.5-flash-lite` | $0.10 / $0.40 | **Cheapest** + FREE tier |
| Grok | `grok-4-1-fast-non-reasoning` | $0.20 / $0.50 | 2M context, fast |
| OpenAI | `gpt-4o-mini` | $0.15 / $0.60 | Popular, reliable |
| Perplexity | `sonar` | $1.00 / $1.00 | Real-time search |
| Anthropic | `claude-haiku-4-5-20251001` | $1.00 / $5.00 | Premium quality |
**Configuration Options:** See `.env.example` for full list of sampling configuration options including:
- API keys for all providers
- Model allowlists
- Rate limiting & quotas
- Content filtering
- System prompt controls
**Auto-discovery (NEW in v0.7.3):** Code-executor automatically discovers and merges:
- `~/.claude.json` (global/personal MCPs)
- `.mcp.json` (project MCPs)
- `MCP_CONFIG_PATH` if set (explicit override, still merges with global)
**No configuration needed** - just add MCPs to either location and code-executor finds them all!
## TypeScript Support
Full type definitions included:
```typescript
import { MCPClientPool, executeTypescript, type ToolSchema } from 'code-executor-mcp';
const pool = new MCPClientPool();
await pool.initialize('/path/to/.mcp.json');
const result = await executeTypescript({
code: `const tools = await discoverMCPTools(); console.log(tools.length);`,
allowedTools: ['mcp__*'],
timeoutMs: 30000
});
```
## Performance
| Metric | Value |
|--------|-------|
| **Token savings** | 98% (141k → 1.6k) |
| **Tool discovery** | <5ms (cached), 50-100ms (first call) |
| **Validation** | <1ms per tool call |
| **Sandbox startup** | ~200ms (Deno), ~2-3s first/~100ms cached (Pyodide) |
| **Test coverage** | 606 tests, 95%+ security, 90%+ overall |
## Documentation
- [AGENTS.md](AGENTS.md) - Repository guidelines for AI agents
- [CONTRIBUTING.md](CONTRIBUTING.md) - Development setup and workflow
- [SECURITY.md](SECURITY.md) - Security model and threat analysis
- [DOCKER_TESTING.md](DOCKER_TESTING.md) - Docker security details
- [CHANGELOG.md](CHANGELOG.md) - Version history
## FAQ
**Q: Do I need to configure each MCP server?**
A: No. Code-executor auto-discovers MCPs from `~/.claude.json` (global) AND `.mcp.json` (project). Just add MCPs to either location.
**Q: How does global + project config merging work?**
A: Code-executor finds and merges both:
- Global (`~/.claude.json`): Personal MCPs available everywhere
- Project (`.mcp.json`): Team MCPs in version control
- Result: All MCPs available, project configs override global for duplicate names
**Q: How does validation work?**
A: AJV validates all tool calls against live schemas. On error, you get a detailed message showing expected parameters.
**Q: What about Python support?**
A: Full Python sandbox via Pyodide WebAssembly. Requires `PYTHON_SANDBOX_READY=true` environment variable. Same security model as Deno (WASM isolation, virtual FS, network restricted). Pure Python only - no native C extensions unless WASM-compiled. See [SECURITY.md](SECURITY.md#-python-executor-security-pyodide) for details.
**Q: Can I use this in production?**
A: Yes. 606 tests, 95%+ coverage, Docker support, audit logging, rate limiting.
**Q: Does this work with Claude Code only?**
A: Built for Claude Code. Untested on other MCP clients, but should work per MCP spec.
## Contributing
Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
**Code Quality Standards:**
- ✅ TypeScript strict mode
- ✅ 90%+ test coverage on business logic
- ✅ ESLint + Prettier
- ✅ All PRs require passing tests
## License
MIT - See [LICENSE](LICENSE)
## Links
- **npm:** https://www.npmjs.com/package/code-executor-mcp
- **Docker Hub:** https://hub.docker.com/r/aberemia24/code-executor-mcp
- **GitHub:** https://github.com/aberemia24/code-executor-MCP
- **Issues:** https://github.com/aberemia24/code-executor-MCP/issues
---
**Built with Claude Code** | Based on [Anthropic's Code Execution with MCP](https://www.anthropic.com/engineering/code-execution-with-mcp)
## Star History
[](https://star-history.com/#aberemia24/code-executor-MCP&Timeline)
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