get_data

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

No annotations are provided, so the description fully explains the tool's behavior, including auto-detection of query types and parameter effects. It lacks information on authentication or rate limits, but for a read-only tool, the disclosure is adequate.

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 and examples, though it is somewhat lengthy. The key purpose is front-loaded, and each example adds value, but some redundancy exists (e.g., repeated query_type explanations).

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 (6 parameters, output schema exists), the description covers usage patterns comprehensively. It lacks details on error handling or performance, but for typical use cases it is sufficiently complete.

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?

With 0% schema description coverage, the description compensates fully by listing all parameters, their defaults, and allowed values. It also provides context-specific examples (e.g., 'query' can be a string or array), adding substantial meaning beyond the raw schema.

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 identifies this as a unified data retrieval tool that handles various query types (gene, protein, region, etc.). It provides extensive examples and distinguishes itself from siblings by being a general-purpose query interface.

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 offers numerous examples showing when to use different query types and parameters, effectively guiding the agent. However, it does not explicitly contrast with sibling tools like advanced_query or smart_search, leaving some ambiguity about when to choose alternatives.

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