_mcp_test_inference_api

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

With no annotations, the description carries the full burden. It reveals the dry-run behavior (curl constructed unless base_url is given), which is key. However, it does not disclose authentication requirements, potential side effects on the container, rate limits, or error handling.

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 sentence, which is short and front-loaded with purpose. However, it is underspecified and could be expanded to cover more aspects without becoming verbose. The conciseness is acceptable but not optimal.

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?

Given the tool has 5 parameters, no schema descriptions, and no annotations, the description is woefully incomplete. It does not explain input requirements, output (despite output schema existing), or configuration details, leaving the agent underinformed.

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%, so the description must compensate. It only hints at base_url's role ('dry_run curl unless base_url provided') but fails to explain the purpose and usage of other parameters like prompts (required), model, max_tokens, and api_key_env.

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's function: sending test requests to a running inference container. It also distinguishes the behavior with 'dry_run curl unless base_url provided', which adds specificity and separates it from sibling tools like _mcp_build_inference_container.

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 no explicit guidance on when to use this tool versus alternatives (e.g., _mcp_run_local_synthetic_train). It only implies testing after building an inference container, but lacks 'when not to use' or context for different scenarios.

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