Bazel MCP Server

Overview

The Bazel MCP Server bridges the gap between AI assistants and the powerful Bazel build system. In many MCP environments, especially those that rely on sandboxed shells or remote sessions, the native Bazel command line is either unavailable or misconfigured. This server eliminates that barrier by exposing a set of well‑defined tools that let an AI agent invoke Bazel operations as if it were running directly in a developer’s terminal. By doing so, it empowers assistants to build, test, query, and manage dependencies in a workspace without needing manual setup or environment tweaks.

At its core, the server offers six tools that mirror common Bazel commands. bazel_build_target compiles specified targets, while bazel_test_target runs their associated tests. bazel_query_target allows the agent to explore dependency graphs, and bazel_list_targets enumerates all available targets within a workspace. The bazel_fetch_dependencies tool pulls in external repositories, and bazel_set_workspace_path lets the agent dynamically switch between workspaces during a session. Each tool accepts an optional field, giving developers the flexibility to pass any Bazel flag—such as or —directly through the MCP interface.

For developers, this translates into a seamless workflow where AI assistants can trigger builds or tests in response to code changes, documentation updates, or even chat prompts. A common scenario is an assistant that watches a repository for new commits and automatically runs on the affected modules, reporting success or failure back to the developer. Another use case involves a code review bot that queries the dependency graph with bazel_query_target to surface potential circular dependencies before a merge. Because the server runs locally, it sidesteps network latency and respects local toolchains, making it ideal for on‑premise CI/CD pipelines or secure environments where external calls are restricted.

Integration is straightforward: any MCP‑enabled client can register the Bazel server in its configuration, after which it can call the provided tools by name. The server’s configuration hierarchy—command line arguments, environment variables, then a config file—ensures that developers can override defaults on the fly without modifying code. Additionally, the ability to set a custom Bazel binary path or workspace root means that teams can support multiple Bazel versions or heterogeneous workspaces within a single AI workflow.

What sets the Bazel MCP Server apart is its focus on reliability and developer ergonomics. By exposing a minimal yet complete set of Bazel operations, it reduces the friction that typically accompanies tool integration in AI systems. The optional mechanism preserves full command‑line flexibility, while the runtime workspace switcher removes the need for static configuration. Together, these features make the server a powerful ally for any team that relies on Bazel and wants to harness AI assistants for automated build, test, and dependency management.