ask

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

No annotations provided, so description carries full burden. It discloses credit consumption, return fields (content, sqlQuery, queryResult), and conversationId persistence. However, it does not clarify whether the SQL run is read-only or could modify the database, leaving some ambiguity.

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?

Three sentences, each serving a distinct purpose: purpose, return values and follow-up usage, and credit consumption with alternative. No redundant information.

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?

Despite no output schema, the description adequately explains return values. It covers purpose, usage, follow-ups, and constraints. For a complex tool, this is complete enough for correct invocation.

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?

All parameters have schema descriptions, and the description adds extra context: message should be plain English, connectionId from list_connections, and conversationId for follow-ups. This adds value beyond 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 states the tool's purpose: 'Ask a question about a database in plain English.' It specifies that the API translates to SQL, runs it, and returns answer, SQL, and results. This differentiates it from sibling tools like run_query, which directly execute SQL.

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 on when to use: for follow-up questions, pass conversationId. Also provides when not to use: 'for repeated/known queries prefer run_query' due to AI credit consumption. This gives clear alternatives and context.

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