About Buildkite MCP Extension for Zed
Buildkite MCP Extension for Zed connects Buildkite CI/CD data to the Zed editor. It lets developers inspect pipelines, builds, jobs, logs, and artifacts alongside Zed's AI features.
Key features
- 01View pipelines and builds
- 02Inspect jobs and logs
- 03Download build artifacts
- 04Use with Zed AI features
Use cases
- 01Check recent pipeline builds in Zed
- 02Inspect logs for a failed job
- 03Review organization pipelines
- 04Download artifacts from a build
MCP support
The Zed extension handles the MCP server and connection details, exposing Buildkite pipelines, builds, jobs, logs, and artifacts to the editor. It requires a Buildkite API token.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEBuildkite MCP Extension for Zed
Integrate Buildkite CI/CD with the Zed editor using the Model Context Protocol (MCP). This extension lets you view and interact with Buildkite pipelines, builds, jobs, and artifacts directly from Zed.
Features
- View Buildkite pipelines and builds
- Inspect jobs and logs
- Download build artifacts
- Seamless integration with Zed's AI features
Requirements
- Buildkite API token (create one here)
Usage
After installing the extension from the Zed marketplace, configure your Buildkite API token in Zed's settings. The extension will handle all server and connection details automatically.
{
"context_servers": {
"mcp-server-buildkite": {
"settings": {
"buildkite_api_token": "your-buildkite-token-here",
}
}
}
}
Example queries:
- "Show me recent builds for my pipeline"
- "Display logs for my failed job"
- "List my organization's pipelines"
License
MIT License — See the LICENSE file for details.
Comments
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.