MCP Server Integration Guide for AI Agents

The strata MCP server exposes infrastructure operations as Model Context Protocol tools, enabling AI assistants (Claude, GitHub Copilot, etc.) to programmatically validate, build, and preview infrastructure deployments. This guide covers setup, security, and real-world integration patterns.

Quick Start

Install the optional MCP dependency:

pip install xyz-strata[mcp]

Start the server (communicates via stdio):

strata mcp serve

Configure your AI client to use the strata MCP server, then ask:

“Validate my deployment YAML and show me what would be deployed.”

The AI now has access to 13 specialized tools and 2 auto-loaded resources.

What’s Possible with MCP

Scenario

Workflow

Executor

Setup

AI scaffolds YAML from template

AI tool: scaffold_file()

Validation

AI validates YAML structure & cross-references

AI tool: validate_file()

Preview

AI shows what build/deploy would produce

AI tool: build_plan(), deploy_plan()

Execute

User approves and runs via CLI

User via CLI only

Monitor

AI checks health, status, and audit logs

AI tool: deploy_status(), audit_query()

Troubleshoot

AI analyzes drift and suggests remediation

AI tool: deploy_status(), audit_query(), validate_file()

Key Principles

  1. AI previews, humans execute

    • MCP tools provide dry-runs and planning only

    • Actual deployments happen via CLI with explicit user confirmation

    • Audit logs track all operations

  2. Local-first security

    • MCP server runs locally via stdio (no network exposure)

    • Secrets are never transmitted via MCP

    • Workspace permissions are enforced by the OS

  3. Full audit trail

    • audit_query() tracks all deploy operations

    • deploy_history() shows recent executions

    • Use with approval workflows for compliance

Documentation Map

Chapter

Topic

Setup & Installation

Prerequisites, installation, server startup, workspace resolution

Claude Deployment Assistant

Full end-to-end example: setup Claude, validate, build, preview, deploy workflow

Copilot Integration

Ideas for enhancing GitHub Copilot extension with MCP-based validation

Security & Workflows

API keys, secrets management, audit logging, approval gates, RBAC patterns

AI-Assisted Troubleshooting

Use case: detect drift, analyze changes, suggest fixes via MCP tools

Tools Reference

Complete API documentation for all 13 MCP tools

Available Tools

Workspace & Schema

  • workspace_status() — Full workspace state, readiness phases, active profile

  • list_schemas() — All supported document kinds

  • get_schema(kind) — JSON Schema for a kind

Authoring

  • scaffold_file(kind, name) — Generate YAML from template

  • validate_file(file_path, deep=False) — Validate YAML against schema

Build Pipeline

  • build_plan(file) — Preview build output (dry-run)

  • build_run(file) — Execute build and generate artifacts

  • build_sbom(file, report="inventory") — Generate SBOM or dependency inventory

Deployment

  • deploy_plan(file, stage=None) — Preview deploy changes (dry-run)

  • deploy_status(file, stage=None) — Current infrastructure state (Terraform outputs)

  • deploy_health(file, stage=None) — Run health checks against deployed stages

Monitoring & Diagnostics

  • deploy_history(lines=20, operation=None) — Recent deployment history

  • audit_query(since=None, stage=None, last=20) — Query deployment logs

Who Should Read This?

  • AI engineers — Building Claude or Copilot integrations with strata

  • DevOps teams — Setting up AI-assisted deployment approval workflows

  • Platform engineers — Using MCP for programmatic infrastructure queries

  • Security architects — Understanding audit, secrets management, and approval gates

Prerequisites

  • strata CLI installed and working (strata --version)

  • Python 3.10+ (same as strata)

  • MCP optional dependency (pip install xyz-strata[mcp])

  • Workspace with .strata/ initialized (strata sln init)

Next Steps

  1. Install & Start the MCP server

  2. Try the Claude example (15 min walkthrough)

  3. Review Security & Workflows before production use

  4. Explore Troubleshooting for operational patterns

Support & Examples