tealflow_search_modules_by_analysis

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety traits are covered. The description adds behavioral detail about the search mechanism ('uses structured analysis type categories combined with text search'), the return structure ('List of matching modules organized by relevance'), and a caveat about flexible dataset types. This goes beyond annotations without contradicting them, though it doesn't specify edge cases such as no matches.

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 long but well-structured with clear sections (Args, Returns, Includes, Note, Categories, Examples). It front-loads the purpose and usage. Some content, like the predefined categories list and examples, is partly redundant, but it serves the agent by providing quick reference without requiring mental inference. Every section contributes to usability.

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 (2 params, 1 required), the description is comprehensive. It explains purpose, parameters, return format, result contents, flexible dataset types, and next-step tool references. The presence of an output schema reduces the need to detail return values, but the description still covers the essential content. No critical gap remains.

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 provides zero description coverage (0%). The description fully compensates by documenting both parameters: analysis_type with a list of examples ('survival', 'safety', 'efficacy', etc.), and response_format with values, default, and semantics ('markdown' for human-readable, 'json' for machine-readable). This is more detailed than most schemas and gives the agent everything needed to choose valid values.

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 opens with a clear verb+resource: 'Search for Teal modules that perform a specific type of analysis.' It explicitly states the tool finds modules based on analysis type, and further differentiates from siblings by noting it helps 'when you know what analysis you need but don't know which module to use.' This clearly distinguishes it from list_modules and get_module_details.

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 explicit usage context: 'when you know what analysis you need but don't know which module to use.' It also names alternatives for follow-up actions: 'Use tealflow_get_module_details to see typical datasets and tealflow_check_dataset_requirements to verify compatibility.' This gives the agent concrete guidance on when and how to use the tool relative to siblings.

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