About The MCP server for GoReleaser
GoReleaser MCP is an MCP server for GoReleaser that helps AI assistants understand and work with GoReleaser configurations. It validates configs, prompts fixes for deprecated options and embeds GoReleaser documentation.
Key features
- 01Validates GoReleaser configuration for errors
- 02Detects deprecated options and prompts fixes
- 03Embedded GoReleaser documentation
- 04update_config prompt for modernizing configs
Use cases
- 01Validating a GoReleaser configuration file with an AI assistant
- 02Migrating older GoReleaser configs to current syntax
- 03Looking up GoReleaser documentation from a coding agent
MCP support
The project is an MCP server that exposes configuration validation tools, embedded documentation and the update_config prompt to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
GoReleaser MCP
Model Context Protocol server for GoReleaser.
A Model Context Protocol (MCP) server for GoReleaser. Provides tools and documentation to help AI assistants understand and work with GoReleaser configurations.
Features
- Configuration Validation: Check GoReleaser configurations for errors and deprecated options
- Deprecation Fixes: Get prompted to fix deprecated configuration options with detailed instructions
- Embedded Documentation: Access GoReleaser documentation directly through the MCP server
- Update Prompt: Use the
update_configprompt to modernize your GoReleaser configuration
Installation
npm install -g @goreleaser/mcp
brew install --cask goreleaser/tap/mcp
winget install goreleaser.mcp
Usage
You can add something like this to your tooling:
{
// ...
"mcp": {
"goreleaser": {
"command": "goreleaser-mcp",
"type": "stdio"
}
}
// ...
}
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New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.