tealflow_discover_datasets

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable operational context: absolute path requirement (and why relative paths fail), case-insensitive extension handling, normalization to uppercase, and common errors. It also explains the output contents, giving full transparency beyond the annotations.

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 sections (Args, Returns, Examples, Common Errors, Note) and front-loads the purpose. It is verbose, but every section adds necessary information given the 0% schema coverage. Some minor redundancy exists (e.g., filename handling is mentioned twice), so it loses one point.

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?

For a tool with 4 parameters, no schema descriptions, but rich annotations and an output schema, the description is complete. It covers purpose, input requirements, parameter semantics, examples, error conditions, filename edge cases, and return contents. There are no significant gaps.

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?

Schema description coverage is 0%, so the description must compensate fully. It does: data_directory is explained as an absolute path with examples, file_formats is defined as a list with null default, pattern is given its default 'AD*', and response_format is explained with 'markdown' vs 'json'. This adds rich meaning beyond the bare 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 opens with 'Discover ADaM datasets in a directory,' a specific verb+resource+scope statement. It further explains that it scans, identifies dataset names, and collects metadata, clearly distinguishing it from sibling tools like list_datasets by emphasizing filename normalization and metadata collection.

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 clear context: it is used for discovering ADaM datasets, requires an absolute path, and handles complex filenames. It warns against relative paths and gives examples. However, it does not explicitly name alternative tools or state when to choose this tool over siblings, so it falls short of a 5.

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