ask_data_analyst

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

With no annotations, the description carries the full burden and excels by disclosing multiple behavioral traits: the verbatim question requirement, the retry-on-empty behavior, the 40-60s latency, and the warning that empty results are indistinguishable from failure. This goes well beyond basic expectations.

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 front-loaded with the core purpose, and every subsequent sentence provides necessary operational detail. It is appropriately sized for the tool's complexity, with no redundant or filler content.

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?

The description covers usage, parameter behavior, failure modes, and gotchas (such as not telling the user 'there is no data'), and an output schema exists to document return values. This makes the tool fully comprehensible for an agent.

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%, but the description explains question, execute_query, session_id, and max_attempts in context, leaving only knowledge_cluster_id to be inferred from the tool's purpose. This substantial compensation warrants a score above baseline, though not 5 because a few parameter specifics (e.g., max_attempts default) are not explicitly stated.

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 states a specific verb ("ask") and resource ("the Data Analyst agent about a knowledge cluster"), clearly distinguishing it from sibling tools that manage sources, pipelines, and clusters. It also provides essential context about verbatim questions, making the purpose unambiguous.

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 gives clear context for when to use the tool (when a question about a knowledge cluster needs an answer) and provides operational guidance on execute_query, session_id, and empty-result behavior. It does not explicitly name alternative tools, so it falls short of a 5.

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