run_sql

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals the text-table return format, truncation flag, auto-mounting, and session availability. It does not explicitly state whether statements are read-only, but the implied use case focuses on querying.

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 three concise sentences, front-loaded with the primary action. Every sentence provides essential information without redundancy, making it efficient and well-structured.

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 semantics, and key behaviors like auto-mounting and truncation. Since an output schema exists, return values need not be detailed. The main gap is the lack of explicit read-only or write clarification, which is minor for this query-oriented tool.

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?

The schema has no descriptions for parameters, but the description compensates fully: 'sql' is explained as the Spark SQL statement, and 'limit' is described as capping returned rows with a default from config (~100). This adds significant meaning 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 runs a Spark SQL statement and returns rows as a text table, using a specific verb and resource. It distinguishes from sibling tools like run_code by emphasizing SQL execution and the text-table output.

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 clear context for when to use the tool, such as referencing Fabric tables and auto-mounting behavior. However, it does not explicitly mention when not to use it or name alternative tools, like run_code.

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