start_vllm

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

With no annotations provided, the description carries the full burden. It discloses behavioral traits like automatic platform/GPU detection and Docker container usage, but lacks details on permissions, side effects (e.g., resource consumption), error handling, or what happens if a container already exists. It's adequate but has gaps for a complex 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 two concise sentences with zero waste—it states the core action and key behavioral traits (platform detection, GPU handling) without redundancy. It's front-loaded with the main purpose, making it easy to scan and understand quickly.

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's complexity (9 parameters, no output schema, no annotations), the description is minimally complete. It covers the basic action and some behavioral context but lacks details on output (e.g., what's returned after starting), error scenarios, or dependencies. It's adequate but could be more informative for such a multifaceted tool.

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 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining interactions between parameters (e.g., 'gpu_memory_utilization' with 'cpu_only'). Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action ('Start a vLLM server in a Docker container') and resource (vLLM server), distinguishing it from sibling tools like 'stop_vllm', 'restart_vllm', or 'vllm_status' which have different purposes. It also mentions platform detection and GPU availability, which adds specificity.

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 implies usage context by mentioning platform detection and GPU availability, suggesting it's for initializing a vLLM server. However, it doesn't explicitly state when to use this tool versus alternatives like 'restart_vllm' or 'vllm_status', nor does it provide exclusions or prerequisites beyond what's implied.

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