Local Bazel MCP Server enables AI agents to build, query, and test targets seamlessly in customized environments
The Bazel MCP Server acts as a bridge, enabling Model Context Protocol (MCP) clients, such as AI applications like Claude Desktop, Continue, and Cursor, to interact with complex build systems managed by Bazel. By providing standardized access to Bazel's rich functionality, this server ensures seamless integration of Bazel-powered tools within broader AI workflows.
The Bazel MCP Server offers a suite of commands that empower AI applications to perform essential tasks in a Bazel workspace:
//
for all)Each command supports an optional additionalArgs
parameter, allowing users to pass additional flags such as --verbose_failures
or --test_output=all
.
The server follows Model Context Protocol (MCP) standards to ensure seamless communication with AI applications. It leverages Bazel’s robust build environment while providing a unified interface for interaction through MCP commands.
graph TB
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server]
C --> D[Bazel Build System]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
graph TD
B[Database] -->|Data Transfer| C[Bazel Build System]
C --> A[MCP Server]
A --> B
style B fill:#e1f2d8
style C fill:#d7e6df
This architecture ensures efficient data exchange between AI applications, the MCP server, and the underlying build system.
Add the following configuration to .cursor/mcp.json
:
{
"mcpServers": {
"bazel": {
"command": "npx",
"args": [
"-y",
"github:nacgarg/bazel-mcp-server",
// Optional flags for path and binary location
"--bazel_path",
"/absolute/path/to/your/bazel/binary",
"--workspace_path",
"/absolute/path/to/your/bazel/workspace"
]
}
}
}
You can run the server directly from GitHub:
# Run directly from GitHub (no installation needed)
npx -y github:nacgarg/bazel-mcp-server
# From source
git clone https://github.com/nacgarg/bazel-mcp-server.git
cd bazel-mcp-server
npm install
npm run build
./dist/index.js
These steps ensure smooth integration with local environments, making it easy to start using the server in AI workflows.
An AI developer can use the bazel_query_target
command to analyze real-time dependency changes within a Bazel workspace. This feature is crucial for maintaining a consistent build environment and avoiding potential issues during model training and deployment.
AI testing frameworks often require precise control over test execution. The bazel_test_target
command allows developers to run comprehensive tests on specified targets, ensuring that their models are robust and reliable before integration.
The Bazel MCP Server supports compatibility with multiple AI clients:
{
"mcpServers": {
"bazel-mcp-server": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-bazel", "--bazel_path=/usr/local/bazel"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
MCP Client | Resources | Tools | Prompts | Status |
---|---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ | Full Support |
Continue | ✅ | ✅ | ✅ | Full Support |
Cursor | ❌ | ✅ | ❌ | Tools Only |
This matrix highlights the supported features for each client, guiding developers in choosing the right setup for their AI projects.
API_KEY
for authenticationMCP_BAZEL_PATH
and MCP_WORKSPACE_PATH
to specify Bazel binary path and workspace directoryAdjust these variables to ensure secure and optimized server operation.
Developers can extend the capabilities of the server by adding custom commands tailored to specific use cases, ensuring a broader range of AI application integrations.
bazel_build_target
and bazel_test_target
commands are designed for direct integration. Users can specify target paths and additional parameters as needed.--workspace_path
flag to set the Bazel workspace root directory. Environment variables can also be configured for flexibility.bazel_fetch_dependencies
command within cron jobs or custom scripts to ensure dependencies are always up-to-date.Contributions are welcome! Developers can contribute fixes and new features through pull requests on GitHub. Join the community to discuss ideas and share insights.
For more information about MCP, visit Model Context Protocol. Explore resources and additional tools that enhance AI application development using Model Context Protocol.
This comprehensive documentation outlines the capabilities and integration pathways for the Bazel MCP Server, making it a valuable asset for developers working on AI applications with complex build systems.
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