AI Agent MCP Server

A Model Context Protocol (MCP) server implementation for AI agent interactions, built with FastAPI and Python 3.11+.

Quick Start

Prerequisites

• Python 3.11+

• Podman

• Git

• NuShell (for setup script)

Setup

# Run automated setup script
nu scripts/setup.nu

# Or run in silent mode (CI/CD)
nu scripts/setup.nu --silent

Development

# Activate virtual environment
source .venv/bin/activate  # macOS/Linux
# or
.venv\Scripts\activate     # Windows

# Run development server
task dev

# Run tests
task test

# Run linting
task lint

Related MCP server: MCP Server Sample

Project Structure

project-root/
├── src/mcp_server/          # Main application package
│   ├── core/                # Core utilities and exceptions
│   ├── models/              # Data models
│   ├── services/            # Business logic
│   ├── repositories/        # Data access layer
│   ├── tools/               # MCP tools
│   ├── api/                 # FastAPI routes and schemas
│   └── utils/               # Utility functions
├── tests/                   # Test suite
│   ├── unit/                # Unit tests
│   ├── integration/         # Integration tests
│   └── e2e/                 # End-to-end tests
├── scripts/                 # Setup and utility scripts
└── docs/                    # Documentation

Deployment

Container images are automatically built on all branches and pushed to GitHub Container Registry only on release/* branches. All container images are scanned for security vulnerabilities before deployment.

Security Scanning

All container builds are automatically scanned for vulnerabilities using Trivy:

• Scope: CVEs in OS packages, Python dependencies, and base images

• Severity Policy:

  • CRITICAL/HIGH: Blocks deployment (build fails)

  • MEDIUM/LOW: Logged as warnings, deployment continues

• Unfixed Vulnerabilities: Ignored (no remediation available)

• Scan Results: Uploaded to GitHub Security tab for centralized tracking

• Database Updates: Trivy vulnerability database refreshed daily

• Documented Exceptions: Tracked in .trivyignore with risk assessments

View vulnerability reports: Repository → Security → Code Scanning

Known Issues (.trivyignore):

• CVE-2025-7709 (libsqlite3-0) - Awaiting Debian security update

• CVE-2025-8869 (pip) - Awaiting Python base image update

Release Process

1. Create release branch: git checkout -b release/v0.1.0

2. Update version in pyproject.toml

3. Push to trigger automated build, security scan, and push: git push -u origin release/v0.1.0

4. Security scan validates image (blocks if CRITICAL/HIGH CVEs found)

5. Container image automatically pushed to ghcr.io with version tags (if scan passes)

Using Pre-built Images

# Pull latest image
podman pull ghcr.io/USERNAME/REPO:latest

# Pull specific version
podman pull ghcr.io/USERNAME/REPO:0.1.0

# Pull by commit SHA
podman pull ghcr.io/USERNAME/REPO:abc123def

# Run container
podman run -d -p 8000:8000 ghcr.io/USERNAME/REPO:latest

Building Locally

# Build with Taskfile
task container:build

# Build with custom tag
TAG=custom task container:build

# Run locally built image
task container:run

Documentation

• Setup Guide - Detailed setup instructions

• Architecture - System architecture

• Contributing - Contribution guidelines

• API Documentation - API reference

Technology Stack

• FastAPI - Modern Python web framework

• Pydantic - Data validation

• UV - Fast Python package manager

• Taskfile - Task automation

• Devbox - Isolated development environment

• Pytest - Testing framework

• Ruff - Linting and formatting

• MyPy - Static type checking

License

TBD