tigergraph__create_data_source

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

No annotations are provided, so the description carries the full burden of explaining behavioral aspects. It mentions that a rejected request will include required keys, which is useful, but it does not state whether creating a data source is idempotent, whether it overwrites existing resources, or whether it requires specific permissions. This leaves some behavioral aspects implicit.

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 a single, well-structured sentence that conveys all necessary information without redundancy. It flows logically from the main action to the caveat about get_data_source_types and the error response behavior, making it easy to scan and understand.

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 complexity (multiple data source types), the description provides adequate context by directing users to get_data_source_types for required keys and examples. It also clarifies the profile parameter's default behavior. It does not mention return values or success indicators, but since there is no output schema, this omission is acceptable.

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 descriptions already cover 100% of parameters, providing detailed explanations for config, profile, data_source_name, and data_source_type. The description adds value by giving concrete examples of config keys (Snowflake, S3) and listing common type values, which helps agents construct valid requests without additional lookups.

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: creating a new data source for various storage and warehouse types. It explicitly lists supported categories and provides a pointer to get_data_source_types for further details, leaving no ambiguity about what the tool does.

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 actionable guidance: call get_data_source_types first if unsure about required keys, and mentions that error responses will include the required keys. It also clarifies the profile parameter and points to list_connections for available profiles, providing sufficient usage context.

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