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TealFlowMCP

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by Appsilon
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tealflow_list_modules

Read-onlyIdempotent

Discover available Teal modules with descriptions and dataset requirements. Filter by package (clinical/general) and category to find suitable analysis modules.

Instructions

List all available Teal modules with their descriptions and dataset requirements.

This tool helps discover what analysis modules are available in the Teal framework. Modules can be filtered by package (clinical vs general) and optionally by category.

Clinical modules are designed for clinical trial reporting and work with ADaM datasets. General modules are for general-purpose data exploration and work with any data.frame.

Args: package (str, optional): Filter by package - 'clinical', 'general', or 'all'. Defaults to 'all'. category (str, optional): Filter by category like 'graphics', 'tables', 'analysis'. Defaults to None. response_format (str, optional): Output format - 'markdown' for human-readable or 'json' for machine-readable. Defaults to 'markdown'.

Returns: str: List of modules with names, descriptions, and required datasets

Dataset requirements may include flexible types:
- BDS_DATASET: Works with any BDS-structured dataset
- BDS_CONTINUOUS: Works with BDS datasets containing continuous data
- BDS_BINARY: Works with BDS datasets containing binary outcomes
- Specific names (ADSL, ADTTE, ADAE): Require exact dataset match

Markdown format:
    # Teal Modules (Package Name)

    ## module_name
    **Description**: Module description
    **Required Datasets**: ADSL, BDS_CONTINUOUS (or "None")

JSON format:
    {
        "modules": [
            {
                "name": "tm_t_ancova",
                "description": "ANCOVA Table",
                "required_datasets": ["ADSL", "BDS_CONTINUOUS"]
            }
        ],
        "count": 10
    }

Examples: - List all clinical modules: package="clinical" - List graphics modules: category="graphics" - Get machine-readable list: response_format="json"

Note: Use tealflow_get_module_details to see typical datasets and detailed requirements for modules with flexible dataset types.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageNoall
categoryNo
response_formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A5/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: describes return formats (markdown/JSON), explains flexible dataset requirement types (e.g., BDS_CONTINUOUS), and shows example output structures. No contradiction.

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

Conciseness5/5

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

The description is front-loaded with the purpose, followed by clear sections (Args, Returns, Examples, Note). It is detailed but every sentence adds value, including concrete format examples that compensate for lack of schema descriptions.

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?

The tool has 3 optional parameters, two output formats, and flexible dataset types. The description covers the listing function, filtering options, return structure, and links to a related tool for deeper details. Despite having an output schema, the description fully explains return values and usage, making it complete.

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?

Even though schema description coverage is 0%, the description's Args section thoroughly explains each parameter, defaults, and allowed values (package, category, response_format). It also includes examples that map parameters to actual usage.

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 'List all available Teal modules with their descriptions and dataset requirements' — a specific verb + resource. It also distinguishes from the sibling tool tealflow_get_module_details by noting its role in listing vs. getting detailed requirements.

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?

Provides explicit usage context: filtering by package and category, with examples ('List all clinical modules', 'List graphics modules'). It also directs users to tealflow_get_module_details for more details, giving a clear alternative.

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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