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tealflow_get_module_details

Read-onlyIdempotent

Get all parameter details, defaults, types, and R help documentation for a Teal module to configure it properly.

Instructions

Get comprehensive details about a specific Teal module including all parameters and R help documentation.

This tool provides complete information about a module's required and optional parameters, their types, default values, descriptions, and official R help documentation. Use this after discovering a module to understand how to configure it properly.

Args: module_name (str, required): Name of the module (e.g., 'tm_g_km', 'tm_t_coxreg', 'tm_g_scatterplot'). response_format (str, optional): Output format - 'markdown' for human-readable or 'json' for machine-readable. Defaults to 'markdown'.

Returns: str: Detailed module information including parameters, datasets, R help, and usage

Includes:
- Module description
- Required datasets (may include flexible types: BDS_DATASET, BDS_CONTINUOUS, BDS_BINARY)
- Typical datasets (examples of datasets that satisfy flexible requirements)
- Dataset requirements (detailed descriptions of what each dataset needs)
- Notes (special considerations like regression type for tm_a_gee)
- Required parameters (no defaults)
- Optional parameters (with defaults)
- Parameter types and constraints
- R help documentation (complete help text from R's help system)
- Usage examples from R documentation

Flexible Dataset Types: - BDS_DATASET: Any BDS-structured dataset (ADLB, ADVS, ADQS, ADEG, ADEX) - BDS_CONTINUOUS: BDS dataset with continuous AVAL (typically ADLB, ADVS, ADQS) - BDS_BINARY: BDS dataset with binary AVAL 0/1 (typically ADRS) - Specific names (ADSL, ADTTE, ADAE): Require exact dataset match

Error Handling: - Returns error if module not found - Suggests similar module names for typos - Provides guidance on correct module names - Falls back gracefully if R help is unavailable

Examples: - Get details for KM plot: module_name="tm_g_km" - Get Cox regression info: module_name="tm_t_coxreg" - Get ANCOVA details (shows BDS_CONTINUOUS): module_name="tm_t_ancova" - Get JSON format: response_format="json"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_nameYes
response_formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description adds extensive behavioral context beyond the annotations: error handling for missing modules, typo suggestions, graceful fallback for R help, and explanation of flexible dataset types. This complements the readOnly/idempotent hints without contradiction.

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 long but well-organized with sections for 'Includes', 'Flexible Dataset Types', 'Error Handling', and 'Examples'. Every section serves a purpose, though some redundancy exists (e.g., repeating parameters list).

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 complexity (module details, dataset requirements, error handling, help docs), the description covers all key aspects: return content, dataset type explanations, error scenarios, and usage examples. It is self-sufficient for an agent to invoke 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?

With 0% schema description coverage, the description fully compensates by explaining module_name with concrete examples and response_format with values, defaults, and use cases. It adds meaning well beyond the bare schema definitions.

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 the tool retrieves comprehensive details for a specific Teal module, including parameters and R help documentation. This distinguishes it from sibling tools like 'list_modules' or 'search_modules_by_analysis'.

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

Usage Guidelines4/5

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

Explicitly says 'Use this after discovering a module to understand how to configure it properly', providing clear usage context. It doesn't name specific alternatives but the sibling tools and phrase 'after discovering' imply the appropriate workflow.

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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