tigergraph__validate_schema_names

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

With no annotations, the description carries the full burden of explaining behavior. It states that the tool validates and returns warnings (as shown in the example), implying a read-only, non-destructive action. However, it does not explicitly state that no modifications are made or describe error handling, which would enhance transparency further.

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 well-structured with clear sections ('Use When', 'Quick Start', 'Related Tools') and is not overly verbose. Every sentence adds value, and the JSON example is concise and illustrative. It avoids redundancy and stays focused on the essential information.

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 needs to clarify what the tool returns. The example indicates that warnings are returned for reserved keywords, but it does not fully specify the return format (e.g., list of warnings, boolean, or error object). This is a minor gap but the example provides enough context for most usage scenarios.

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 provides 100% coverage with descriptions for all three parameters, and the description adds value by indicating that the formats are the same as create_graph, linking to a sibling tool for detailed structure. This is helpful, though it does not fully explain the internal structure of the array items (which are undefined in the schema). The pointer to create_graph compensates for this gap.

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: to validate vertex type names, edge type names, attribute names, and graph names against GSQL reserved keywords and naming conflict rules. It uses a specific verb ('validate') and identifies the exact resources involved, making it easy to distinguish from other tools.

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 explicitly provides usage guidance with a 'Use When' section, listing three concrete scenarios (before create_graph, checking for reserved keywords, and checking attribute name collisions). It also names related tools (create_graph, get_graph_schema), giving clear context for when to use this tool versus alternatives.

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