_mcp_build_docker_image

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses the two modes of operation (dry-run vs actual build) and that it both renders and builds. However, it lacks details on side effects (e.g., overwriting files, need for Docker daemon) which could be expected for a build tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no unnecessary words. It immediately communicates the core action and the key behavioral nuance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters and a complex build process, the description is minimal. It does not explain prerequisites (e.g., existence of Dockerfile.train), output details, or how the dry-run mode works. While an output schema exists, the agent still lacks guidance on parameter semantics and configuration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 4 parameters with 0% description coverage, and the description adds no information about any parameter (tag, base_image, project_id, cache_models). The agent must rely solely on parameter names, which may be insufficient for correct usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool renders a Dockerfile and executes a docker build command, using specific verbs 'Render' and 'emit/execute'. It distinguishes from sibling tools like '_mcp_build_inference_container' and 'test_docker_build' by focusing on building the training image.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a key usage condition: 'dry-run unless local_python+docker configured'. This tells the agent when the tool will actually execute versus just emitting the command. However, it does not explicitly compare to alternatives or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.