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. It mentions AI processing and returns a dict with answers per file, but lacks critical behavioral details: whether this is a read-only operation, potential costs/rate limits, authentication requirements, error handling, or how the AI processes multiple files (e.g., sequentially or in parallel).

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 clear purpose statement followed by Args and Returns sections. It's relatively concise, but the Args section could be more streamlined (e.g., merging example into the parameter description). No wasted sentences, but minor verbosity in formatting.

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 3-parameter AI tool with no annotations and no output schema, the description provides basic purpose and parameter info but is incomplete. It misses behavioral context (costs, limits, auth), output structure details beyond 'dict', and differentiation from siblings. It's minimally adequate but has clear gaps given the complexity.

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 basic semantics for all three parameters: file_ids as a list of IDs with an example, prompt as the question, and ai_agent_id as optional with default behavior. However, it lacks details on ID formats, prompt constraints, or agent ID sourcing, leaving gaps in parameter understanding.

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 multiple files using AI.' It specifies the verb ('ask'), resource ('multiple files'), and method ('using AI'). However, it doesn't explicitly differentiate from its sibling 'box_ai_ask_file_single_tool' beyond the 'multiple' vs implied 'single' distinction.

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 the sibling 'box_ai_ask_file_single_tool' or other AI tools like 'box_ai_ask_hub_tool', nor does it specify prerequisites, constraints, or typical use cases.

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