get_label_values

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

No annotations are provided, so the description carries the full burden. It implies a read-only operation ('discover') and hints at use cases like UI filters, but doesn't disclose behavioral traits such as authentication needs (though 'userId' is required), rate limits, or what happens if invalid inputs are provided. It adds some context but lacks depth for a tool with no annotations.

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 concise and well-structured in two sentences: the first states the purpose with examples, and the second provides usage guidelines. Every sentence adds value without redundancy, making it efficient and front-loaded.

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 tool's moderate complexity (3 required parameters, no annotations, but with an output schema), the description is reasonably complete. It covers purpose and usage, and since an output schema exists, it doesn't need to explain return values. However, it could improve by addressing authentication or error handling, given the lack of annotations.

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 input schema has 100% description coverage, with clear parameter descriptions (e.g., 'The graph ID', 'The label to fetch values for'). The description adds no additional parameter semantics beyond what the schema provides, such as explaining label examples further or clarifying user ID requirements. Baseline 3 is appropriate given high schema coverage.

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: 'Discover valid values for a specific node label' with examples like 'RetailerType, Technology'. It specifies the action (discover) and resource (node label values), but doesn't explicitly differentiate from sibling tools like 'get_node' or 'search_graph' that might also retrieve node-related information.

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 usage context: 'Use for discovery, UI filters, and category exploration.' This gives practical scenarios for when to use the tool. However, it doesn't explicitly state when NOT to use it or name alternatives among the sibling tools, such as when to prefer 'get_node' for node details instead.

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