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

With no annotations, the description carries the full burden and does well: it discloses the return shape (child names, counts, parent path) and the round-trip cost model (1+N vs single call). It stops short of stating permission/auth requirements or pagination/size limits for large depth values.

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?

Purpose and routing are front-loaded in the first two sentences, and the longer example paragraph justifies its length by encoding a non-obvious combined-call pattern. Slightly verbose, but every sentence contributes.

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?

There is no output schema, so the description must describe returns, and it does (child names, counts, parent path). Combined with the usage routing and the optimization guidance, an agent has everything needed to call 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?

Schema coverage is already 100%, so baseline would be 3. The description adds real meaning beyond the schema by explaining the depth+attributes interaction and giving a concrete worked example (depth=2, attributes=["AI TEXT"] on 'Start here'), which clarifies how depth and attributes compose.

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?

States a specific verb and resource ('Drill into a named set') and precisely enumerates what is returned: child names with counts plus the parent path. It distinguishes itself from siblings by positioning itself after describe_database and contrasting with describe_query.

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?

Explicitly states when to use it ('after describe_database to explore a specific set') and names the alternative path (one describe_query per child). It also gives a clear preference rule for topic reads, telling the agent exactly which call pattern to choose.

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