ask_agent

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

With no annotations, the description fully discloses the tool's behavior: it queries a live database, detects anomalies, may pause for human review, and returns either a final answer or a review card. It also specifies the execution environment (LangGraph at LANGGRAPH_URL). No contradictions with annotations (none provided).

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 the purpose front-loaded, followed by usage guidelines and specific skill details. While the list of skills and keywords is somewhat lengthy, it is relevant and aids the agent in matching questions. Every sentence serves a purpose, with no wasted words.

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 the tool's complexity, two parameters, and no output schema or annotations, the description provides essential context: how the agent works, the nature of results, and the optional thread_id. It could mention error handling or response structure in more detail, but overall it is complete enough for effective use.

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?

Although schema description coverage is 0%, the description adds meaning to both parameters: it explains the `question` as a natural-language query and `thread_id` as optional for continuing a conversation. This compensates for the lack of parameter descriptions in the schema, though more detail on `question` format would strengthen it.

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: asking a natural-language question to the LangGraph skills agent and rendering the response as a dashboard card. It specifies the loaded skills and differentiates from siblings by focusing on question-answering rather than review actions.

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?

Explicit guidance is provided on when to call this tool, including that it should be used even if the user does not explicitly mention the agent. It advises against answering from memory and instructs routing to the agent. However, it does not explicitly exclude scenarios or mention alternatives beyond the listed skills.

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