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edgarrmondragon

LimeSurvey MCP Server

add_group

Create a new group in a LimeSurvey survey to organize questions and structure your questionnaire effectively.

Instructions

Add a group to a LimeSurvey survey.

Args:
    sid: The survey ID.
    group_data: The group data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sidYes
group_dataYes

Implementation Reference

  • main.py:437-447 (handler)
    The MCP tool handler for 'add_group'. This function is decorated with @mcp.tool(), defining the tool's implementation as a wrapper around the LimeSurvey client's add_group method. The type hints and docstring provide the input schema.
    @mcp.tool()
    def add_group(sid: int, group_data: dict[str, Any]) -> int:
        """Add a group to a LimeSurvey survey.
    
        Args:
            sid: The survey ID.
            group_data: The group data.
        """
        with get_client() as client:
            return client.add_group(sid, group_data)
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Add' implies a write/mutation operation, the description doesn't address permissions needed, whether the operation is idempotent, what happens on failure, or what the response contains. This is inadequate for a mutation tool with zero annotation coverage.

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 appropriately brief (two sentences plus parameter list) and front-loaded with the core purpose. However, the parameter descriptions are overly terse ('The group data' adds almost no value), slightly reducing efficiency.

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

Completeness2/5

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

For a mutation tool with 2 parameters (including a nested object), 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks essential information about parameter details, behavioral traits, error handling, and return values, making it inadequate for reliable tool invocation.

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

Parameters2/5

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. It lists the two parameters ('sid' and 'group_data') but provides minimal semantic information: 'sid' is described as 'The survey ID' (basic) and 'group_data' as 'The group data' (tautological). This doesn't explain what constitutes valid group data, required fields, or format expectations.

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

Purpose4/5

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

The description clearly states the action ('Add a group') and target resource ('to a LimeSurvey survey'), providing specific verb+resource information. However, it doesn't differentiate from sibling tools like 'import_group' or 'set_group_properties', which handle related group operations.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'import_group' or 'set_group_properties'. It mentions no prerequisites, constraints, or typical use cases, leaving the agent without contextual usage information.

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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