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

Annotations only indicate readOnlyHint=false and destructiveHint=false, so the description carries the burden of explaining behavior. It adds valuable context: the dependency on get_intent_topics, AND logic across criteria, examples of include/exclude semantics, and the expected return content (ID, size estimation, creation status). It falls slightly short only because it does not discuss side effects or operational limits, though the annotations already establish this is a write but non-destructive operation.

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 not appropriately sized. It is very long and repetitive: the CONTACTS CRITERIA section appears twice with almost identical parameter lists, and several ID labels and examples are duplicated from the input schema. While the most important prerequisite is front-loaded, the redundant sections make the definition harder to scan than it should be.

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 13-parameter tool with no output schema, the description is remarkably complete: it covers prerequisites, when to use, when not to use, alternative tools, full valid value sets, examples, AND logic, and return information. An agent has enough context to decide whether to invoke it and how to fill its parameters correctly.

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?

Even though schema description coverage is 100%, the description adds substantial meaning beyond the schema: human-readable labels for industry, job function, and seniority IDs; concrete examples; include/exclude semantics; AND-combination logic; and the instruction to source topic values from get_intent_topics. This goes well beyond what the structured schema alone provides.

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 creating Bombora buyer intent audiences on the Metadata platform, and further specifies that it targets B2B audiences by buyer intent topics, firmographics, contact attributes, and geography. This is a specific verb-resource pair that distinguishes it from sibling audience creation tools like create_firmographic_audience or create_g2_intent_*.

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 explicit when-to-use guidance, including a mandatory prerequisite: call get_intent_topics first, and if no topics are returned, do NOT use this tool. It also names the preferred alternative, create_firmographic_audience, making the decision boundary entirely clear.

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