_mcp_build_inference_container

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

The description discloses the dry-run behavior unless local Docker is configured, which is a key behavioral trait. However, it omits other details like error handling, required permissions, or side effects (e.g., generating files). With no annotations, the description bears the full burden and only partially fulfills it.

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, compact sentence that conveys the core functionality and key behavioral note without any extraneous information. It is well-structured and front-loaded.

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?

Despite having a simple signature, the tool has three parameters with no schema descriptions and no parameter details in the description. The output schema exists but its content is unknown. The description fails to fully orient the agent on required inputs and expected outputs.

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?

Schema description coverage is 0%, yet the description provides no explanations for the three parameters (engine, model_path, project_id). The agent is left with only names and types, which is insufficient for correct invocation.

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 emits a docker build command, with a dry-run fallback. It distinguishes itself from sibling tools like _mcp_build_docker_image and test_docker_build by focusing on the inference container build, but does not explicitly contrast them.

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?

No guidance on when to use this tool versus alternatives such as _mcp_build_docker_image or test_docker_build. The description does not specify prerequisites (e.g., having a Dockerfile.infer) or conditions for actual execution beyond local Docker configuration.

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