Lightweight PowerShell MCP framework

The PowerShell MCP module lets you build Model Context Protocol (MCP) servers directly from PowerShell functions.

Instead of writing a server from scratch, you define functionality as PowerShell functions and expose it to MCP clients through JSON-RPC 2.0 over stdio transport.

This approach is useful for development, infrastructure management, and CI/CD automation scenarios where PowerShell scripts already exist and need to be exposed as structured tools for AI assistants.

PowerShell is well-suited for implementing MCP servers for automation processes because it combines a rich automation ecosystem with native cross-platform capabilities.

- Pure PowerShell implementation– no external runtime dependencies, leverages native PowerShell capabilities.
- Stdio-based MCP server– stdio transport implementation for integration with MCP clients such as GitHub Copilot and Gemini CLI.
- Cross-platform support– consistent behavior across platforms (Windows, Linux, macOS) with the same codebase.
- Automatic schema generation– converts PowerShell function parameters, validation attributes, and Comment-Based Help into JSON Schema definitions for MCP tools.
- Parameter validation– leverages PowerShell's declarative validation attributes for type-safe MCP tool inputs.

The module implements the MCP lifecycle on top of stdio and JSON-RPC 2.0:

- stdio transport– Communication happens exclusively through stdin/stdout; no HTTP endpoints are required.
- JSON-RPC 2.0– Requests and responses follow the JSON-RPC 2.0 specification with strict validation and predictable error codes.
- Tool discovery(tools/list) – Discovers and exports selected PowerShell functions as MCP tools, including their schema and metadata.
- Tool execution(tools/call) – Incoming MCP tool calls are mapped to PowerShell function invocations, with automatic parameter binding and validation.
- Initialization and shutdown– Supports the core MCP methods.

- Sanitization of incoming requests to prevent unintended command execution triggered by AI-generated inputs.
- Function parameter validation ensures that inputs match the expected types, ranges, and patterns.
- Leverages native PowerShell security features such as script signing and execution policies.
- The MCP server can be run inside a Docker container for additional isolation when exposing automation capabilities.

Typical scenarios include automating repetitive tasks, exposing existing scripts as tools for AI assistants, and integrating with local tools or cloud services:

- Converting existing admin or maintenance scripts into reusable MCP tools.
- Prototyping developer utilities specific to a team or project.
- Building ad-hoc tools during a development session and immediately exposing them to AI coding assistants.

- Orchestrating deployment pipelines through MCP tools backed by existing PowerShell scripts.
- Exposing infrastructure-as-code workflows (Terraform, Ansible) via PowerShell wrappers.
- Managing build artifacts, release gates, and deployment validation steps as MCP tools.

- Managing cloud resources (Azure PowerShell, AWS Tools for PowerShell, Google Cloud modules).
- Automating on-premises environments: Active Directory, Exchange, VMware, network devices, and more.
- Implementing monitoring and alerting workflows using existing PowerShell-based monitoring modules.

- Integrating with external systems via REST or SDK-based PowerShell modules.
- Implementing compliance checks and audit pipelines using security-focused PowerShell modules.
- Generating reports and analytics by combining data-processing cmdlets with MCP tooling.

Any MCP-compliant client that implements the MCP stdio transport and the core MCP methods should be able to connect to a PowerShell MCP server with appropriate configuration.

The repository currently includes documentation and examples for the following MCP clients:

This example shows how to expose a simple PowerShell function as an MCP tool.

⏺Create a PowerShell scriptthat defines one or more functions and imports the PowerShell MCP module.

To configure an MCP client, see the official documentation for the related client:

⏺Call the MCP toolfrom your MCP client (e.g., GitHub Copilot in Visual Studio Code):

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