Auggie (Augment Code)▌
by saharmor
Run Augment Code with Auggie CLI—your AI powered coding assistant and AI code helper for smarter, faster coding.
Run Augment Code as a coding agent via the Auggie CLI
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers wanting AI-powered code understanding
- / Code review and exploration workflows
- / Automated code implementation tasks
capabilities
- / Ask questions about repository code using Auggie's context engine
- / Implement code changes with dry-run mode by default
- / Query codebase through natural language
- / Generate code modifications based on descriptions
what it does
Integrates Augment Code's Auggie CLI to answer questions about your codebase and implement code changes through an MCP interface.
about
Auggie (Augment Code) is a community-built MCP server published by saharmor that provides AI assistants with tools and capabilities via the Model Context Protocol. Run Augment Code with Auggie CLI—your AI powered coding assistant and AI code helper for smarter, faster coding. It is categorized under developer tools.
how to install
You can install Auggie (Augment Code) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Auggie (Augment Code) is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Auggie MCP Server
Minimal MCP server exposing Auggie CLI as tools for Q&A and code implementation.
Tools
- ask_question: Repository Q&A via Auggie’s context engine.
- implement: Implement a change in the repo; dry-run by default.
Requirements
- Node.js 18+
- Python 3.10+ available on the system (used internally; no manual setup needed)
- Auggie CLI installed (check by running
auggie --version) - see installation guide
Authentication (AUGMENT_API_TOKEN)
Retrieve your token via the Auggie CLI:
# Ensure Auggie CLI is installed and on PATH
auggie --version
# Sign in (opens browser flow)
auggie login
# Print your token
auggie --print-augment-token
Provide the token in either of these ways:
- Cursor/Claude config (recommended): set it under
envfor the server
{
"mcpServers": {
"auggie-mcp": {
"command": "npx",
"args": ["-y", "auggie-mcp@latest"],
"env": { "AUGMENT_API_TOKEN": "YOUR_TOKEN" }
}
}
}
- Shell environment (macOS/Linux)
One-off for a single command:
AUGMENT_API_TOKEN=YOUR_TOKEN npx -y auggie-mcp --setup-only
Persist for future shells (zsh):
echo 'export AUGMENT_API_TOKEN=YOUR_TOKEN' >> ~/.zshrc
source ~/.zshrc
Security tip: never commit tokens to source control. Prefer per-machine environment variables or your client's secure config store.
Configure Clients
Cursor via npx
Use this MCP config in Cursor (global or per-project):
{
"mcpServers": {
"auggie-mcp": {
"command": "npx",
"args": ["-y", "auggie-mcp@latest"],
"env": { "AUGMENT_API_TOKEN": "YOUR_TOKEN" }
}
}
}
This will:
- download the wrapper package,
- create a local Python venv inside the package,
- install
requirements.txt, and - launch the Python server in
stdiomode.
Quick test via npx (terminal)
# Install deps into the package's local venv (no global installs)
npx -y auggie-mcp --setup-only
# Run the server (stdio). Useful for quick smoke-tests.
npx -y auggie-mcp
# Optional: start HTTP mode for manual debugging
npx -y auggie-mcp -- --http
Claude Desktop (macOS)
Edit ~/Library/Application Support/Claude/claude_desktop_config.json and add:
{
"mcpServers": {
"auggie-mcp": {
"command": "npx",
"args": ["-y", "auggie-mcp@latest"],
"env": { "AUGMENT_API_TOKEN": "YOUR_TOKEN" }
}
}
}
Security and permissions
- Default:
implementruns in dry‑run mode. No files are written, no shell runs; you get a proposed diff. - Enable writes: set
dry_run: false. - Recommendation: use a feature branch and review the diff before merging.
FAQ
- What is the Auggie (Augment Code) MCP server?
- Auggie (Augment Code) is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Auggie (Augment Code)?
- This profile displays 42 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★42 reviews- ★★★★★Olivia Tandon· Dec 28, 2024
We wired Auggie (Augment Code) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Dev Srinivasan· Dec 16, 2024
Auggie (Augment Code) has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Dev Shah· Dec 12, 2024
Auggie (Augment Code) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Chaitanya Patil· Dec 8, 2024
According to our notes, Auggie (Augment Code) benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Piyush G· Nov 27, 2024
We wired Auggie (Augment Code) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Olivia Gupta· Nov 19, 2024
According to our notes, Auggie (Augment Code) benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Yusuf Ghosh· Nov 15, 2024
Useful MCP listing: Auggie (Augment Code) is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Dev Liu· Nov 7, 2024
Auggie (Augment Code) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Dev Chawla· Oct 26, 2024
We wired Auggie (Augment Code) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Shikha Mishra· Oct 18, 2024
Auggie (Augment Code) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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