tigergraph__generate_gsql

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

With no annotations provided, the description carries the full burden. It clearly states that the tool only generates a query (not executes it) and mentions the use of an LLM. It does not disclose potential side effects like costs or latency, but for a generation tool this is acceptable and the core behavior is transparent.

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, around 50 words, and every sentence adds value: purpose, usage scenario, next step (execute with gsql), and an optimization hint. No filler or repetition.

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 simplicity and lack of an output schema, the description provides sufficient context: what it generates, how to use it effectively, and how the result can be used downstream. It does not cover edge cases or error conditions, but these are not critical for a straightforward generation tool.

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 already provides descriptions for all three parameters (100% coverage), so the baseline is 3. The description adds value by recommending that graph_name be provided 'for best results' and clarifying that schema will be fetched, enhancing the semantic understanding beyond the schema alone.

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: 'Generate a GSQL query from a natural language description using an LLM.' It also specifies when to use it ('when you need to create a GSQL query but are unsure of the exact syntax'), making the intent unmistakable and distinct from query execution 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?

Provides explicit usage guidance: 'Use this tool when you need to create a GSQL query but are unsure of the exact syntax.' It also advises providing graph_name for better accuracy and notes that the generated query can be executed using the gsql tool, giving clear context for when and how to use it.

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