MCP Server Box

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that an AI agent processes the question and returns a response, but lacks details on permissions, rate limits, response format, error handling, or whether this is a read-only or mutating operation. For an AI query tool with zero annotation coverage, this is insufficient.

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 well-structured with a purpose statement followed by Args and Returns sections. It's appropriately sized with no redundant information. However, the 'ctx' parameter explanation is vague ('The context object containing the request and lifespan context'), which slightly reduces efficiency.

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 3 parameters with 0% schema coverage and no output schema, the description does a decent job explaining parameters but lacks behavioral context. It doesn't cover response structure, error cases, or operational constraints. For an AI tool with no annotations, this leaves significant gaps in understanding how to use it effectively.

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 provides clear explanations for all three parameters: hub_id (ID of the hub), prompt (the question), and ai_agent_id (optional AI agent ID with default behavior). This adds meaningful context beyond the bare schema, though it doesn't specify format examples beyond '1234567890' for hub_id.

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 purpose: 'Ask a question about a hub using AI.' It specifies the verb ('ask'), resource ('hub'), and method ('using AI'). However, it doesn't explicitly differentiate from sibling tools like box_ai_ask_file_multi_tool or box_ai_ask_file_single_tool, which ask questions about files rather than hubs.

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 guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or compare it to other AI question tools in the sibling list. The agent must infer usage from the tool name and parameters alone.

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