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

Annotations already declare the tool safe and read-only. The description adds useful behavioral context by stating that it returns summarised, cited insights from a curated knowledge base, which helps an agent anticipate the nature and format of the response beyond what the schema conveys.

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?

Two concise sentences cover both the tool's purpose and its output style with no filler or repetition. The key scope information is front-loaded, making it easy for an agent 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?

For a simple query-and-answer tool with a clear schema and safe annotations, the description is sufficiently complete: it states what the user can ask, what topics are in scope, and what the response will look like. It doesn't need to explain return values in depth since the output format is briefly characterised.

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 explains query, email, and top_k. The description does not add parameter-specific meaning, but none is needed because each parameter, including the email free-tier detail, is well documented in the schema.

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 identifies the tool as a question-answering resource for the creator economy, naming specific topic areas such as audience growth, monetisation, and personal brand. While it doesn't explicitly contrast with sibling specialists like ergora_content or ergora_marketing, the domain and output style make the purpose reasonably distinct.

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 when to use the tool: when the user asks about creator-economy topics. However, it gives no explicit guidance on when not to use it or which sibling tool to prefer for overlapping topics like content or marketing, leaving the agent to infer boundaries.

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