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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context by explaining that the tool traverses a knowledge graph and what each input mode returns, including the '+why' reasoning for joinable datasets.

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 compact and front-loaded with the core operation, followed by two scannable parameter bullets and a concise args line. Every sentence contributes meaningful information without redundancy.

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 low parameter count, the read-only annotations, and the presence of an output schema, the description covers what an agent needs to select and invoke the tool correctly. It explains both input modes, their purposes, and example values, leaving no critical gap.

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 description coverage is 0%, so the description carries the full burden. It explains each parameter's exact output, gives concrete examples, and clarifies that the arguments may be used together or separately ('and/or').

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 action ('Traverse the knowledge graph') and the resource it operates on, with per-parameter expected outputs. It does not explicitly differentiate from sibling tools such as supply_chain_read or query_dataset, so differentiation is left implicit.

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 gives clear context for when to use the tool: 'to plan multi-table analysis' via dataset_id and 'supply-chain reasoning' via ticker. It does not name alternative sibling tools or state when not to use this tool, so it stops short of full routing guidance.

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