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tealflow_check_dataset_requirements

Read-onlyIdempotent

Verify dataset availability for a Teal module against your list. Receive a clear compatibility report with missing datasets and suggestions.

Instructions

Check if required datasets are available for a specific module.

This tool validates whether you have all necessary datasets before attempting to use a module. It compares the module's dataset requirements against your available datasets and provides clear feedback. Supports flexible dataset types that match multiple dataset names.

IMPORTANT: Before checking compatibility, use tealflow_get_dataset_info to verify that your datasets have the correct structure and data types for the module:

  • For BDS_CONTINUOUS modules (ANCOVA, MMRM): Verify AVAL is continuous numeric

  • For BDS_BINARY modules (logistic GEE): Verify AVAL is binary 0/1

  • For all BDS modules: Verify required columns exist (PARAMCD, AVISIT, USUBJID, etc.)

Args: module_name (str, required): Name of the module to check dataset requirements for. available_datasets (list[str], required): List of available dataset names (e.g., ['ADSL', 'ADLB', 'ADVS']). response_format (str, optional): Output format - 'markdown' for human-readable or 'json' for machine-readable. Defaults to 'markdown'.

Returns: str: Compatibility report with status and missing/matched datasets

Includes:
- Compatibility status (compatible/incompatible)
- List of required datasets (with flexible types if applicable)
- Matched datasets (which available datasets satisfy flexible requirements)
- Typical datasets (examples for flexible types)
- Dataset requirements (detailed descriptions)
- Notes (special considerations)
- List of missing datasets (if any)
- Suggestions for alternatives and guidance

Flexible Dataset Type Matching: - BDS_DATASET: Matches ADLB, ADVS, ADQS, ADEG, ADEX (any BDS structure) - BDS_CONTINUOUS: Matches ADLB, ADVS, ADQS (BDS with continuous data) - BDS_BINARY: Matches ADRS (BDS with binary outcomes) - Specific names: Must match exactly (ADSL matches ADSL, ADTTE matches ADTTE)

Examples: - Check KM plot (specific dataset): module_name="tm_g_km", available_datasets=["ADSL", "ADTTE"] - Check ANCOVA (flexible BDS_CONTINUOUS): module_name="tm_t_ancova", available_datasets=["ADSL", "ADLB"] - Check with custom datasets: module_name="tm_g_km", available_datasets=["ADSL", "ADTTE", "ADLB"]

Recommended Workflow: 1. Call tealflow_discover_datasets to find available datasets 2. Call tealflow_get_dataset_info to verify structure and data types 3. Call this tool to check compatibility 4. If compatible and data types verified, proceed with module generation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_nameYes
response_formatNomarkdown
available_datasetsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavior beyond annotations by detailing what the tool does (compares requirements, returns compatibility report) and how it handles flexible dataset types. It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but well-structured with clear sections (Args, Returns, Flexible Dataset Type Matching, Examples, Recommended Workflow). It front-loads the core purpose and then provides necessary detail. Some redundancy exists (e.g., repeated mention of verification), but overall every section serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity and schema with 0% property descriptions, the description delivers complete context: parameter details, return content, flexible matching rules, examples, and a recommended workflow. The presence of an output schema reduces the need to explain return format, but the description still covers it, ensuring the agent can use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is 0%, so the description fully compensates. It explicitly explains module_name, available_datasets with an example list, and response_format with allowed values and default. Examples further illustrate parameter usage, making parameter semantics crystal clear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb+resource: 'Check if required datasets are available for a specific module.' It distinguishes itself from sibling tools by focusing on validating module requirements rather than listing or discovering datasets. The first sentence alone captures the purpose unambiguously.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit workflow guidance: 'Recommended Workflow' includes steps to call discover_datasets, get_dataset_info, then this tool. It also advises using get_dataset_info before checking compatibility, clarifying the proper sequence and relationship to alternatives. This is rare and valuable.

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

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