zuckerbot_upload_business_context

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 discloses that raw text is accepted and that insights will be extracted, but omits important behavioral details such as whether the uploaded data overwrites previous entries, what the success response looks like, any size limits, or authentication requirements. As a result, an agent might not fully understand the tool's side effects or constraints.

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 consists of two tightly packed sentences with no wasted words. The first sentence clearly states the action and purpose, and the second adds a usage instruction. Every phrase earns its place, making it easy to parse 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 there is no output schema and no annotations, the description should explain what the tool returns or how the system reacts. It only implies future insight extraction ('so ZuckerBot can extract...') but doesn't state the immediate outcome (e.g., whether a confirmation message or ID is returned). It also lacks details on limitations (e.g., max content size) that would be needed for a complete understanding.

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?

The schema covers all 4 parameters with descriptions, so baseline is 3. The description adds value by clarifying that content is 'raw text content — not binary files,' which is more specific than the schema's 'File content as text.' It also provides context for the 'context_type' enum by listing example types, helping the agent choose appropriate values.

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 action ('upload a text document'), the resource ('business context'), and the purpose ('so ZuckerBot can extract structured planning insights'). It enumerates specific document types (ad performance, brand guidelines, etc.), making it unambiguous. Among many sibling tools, this one is uniquely about uploading text for insight extraction, differentiating it effectively.

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 explicitly states when to use the tool: 'Use this when the business has existing performance data or brand docs that should inform campaign strategy.' It also clarifies what not to do: 'Accepts raw text content — not binary files,' which implicitly guides against using it for non-text files. While it doesn't name alternatives, the context of siblings like 'zuckerbot_upload_creative' provides differentiation.

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