check_directlake_fallback

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 explicitly states 'Read-only,' which is a key safety trait, and describes the actions (reading DMV, listing objects). It does not cover prerequisites or exact return format, but the read-only nature is well conveyed.

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 two concise sentences, front-loaded with the primary action. Every word adds value, and it avoids redundancy. It is appropriately sized for the tool's complexity.

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 tool has one parameter, no annotations, and no output schema, so the description must do heavy lifting. It covers the purpose, method, and read-only trait, but it omits explanation of sessionId and does not describe the output structure beyond 'list model objects.' This leaves gaps for an agent to use it 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?

The schema has one parameter (sessionId) with no description, and the tool description does not mention it at all. With 0% schema description coverage, the description fails to explain what sessionId refers to or how it should be used, providing no semantic value for the parameter.

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 a specific verb ('Diagnose') and resource ('Direct Lake DirectQuery fallbacks'), and details the exact actions: reading the fallback-reason DMV and listing unsupported model objects. This distinguishes it from sibling tools like 'warm_directlake_cache' or 'bridge_status' by focusing on diagnosis of fallbacks.

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 implies use when there is a Direct Lake DirectQuery fallback to diagnose, and states it is read-only. It does not explicitly mention exclusions or alternatives, but the purpose context is clear enough for an agent to differentiate from other tools.

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