Skip to main content
Glama
edgarrmondragon

LimeSurvey MCP Server

export_timeline

Export submission timeline data from a LimeSurvey survey with configurable date ranges and aggregation periods for analysis.

Instructions

Export timeline for a LimeSurvey survey.

Args:
    sid: The survey ID.
    period: The granularity level for aggregation submission counts ('day' or 'hour').
    start_date: The start datetime (YYYY-MM-DDTHH:MM:SS) in any valid ISO 8601 format.
    end_date: The end datetime (YYYY-MM-DDTHH:MM:SS) in any valid ISO 8601 format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sidYes
periodYes
start_datetimeYes
end_datetimeNo

Implementation Reference

  • main.py:352-366 (handler)
    The handler function for the 'export_timeline' MCP tool. Decorated with @mcp.tool(), which registers it with the FastMCP server. Parses input datetimes and delegates to the citric Client.export_timeline method.
    @mcp.tool()
    def export_timeline(sid: int, period: str, start_datetime: str, end_datetime: str = None) -> str:
        """Export timeline for a LimeSurvey survey.
    
        Args:
            sid: The survey ID.
            period: The granularity level for aggregation submission counts ('day' or 'hour').
            start_date: The start datetime (YYYY-MM-DDTHH:MM:SS) in any valid ISO 8601 format.
            end_date: The end datetime (YYYY-MM-DDTHH:MM:SS) in any valid ISO 8601 format.
        """
        with get_client() as client:
            start_dt = datetime.fromisoformat(start_datetime)
            end_dt = datetime.fromisoformat(end_datetime) if end_datetime else None
            return client.export_timeline(sid, period, start_dt, end_dt)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is an export operation but doesn't clarify whether this creates files, returns data directly, requires specific permissions, or has rate limits. The mention of 'aggregation submission counts' hints at data transformation, but behavioral aspects like output format, side effects, or authentication requirements remain unspecified.

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 perfectly structured and economical. The first sentence states the core purpose, followed by a clean parameter documentation section. Every sentence earns its place, with no redundant or verbose language. The information is front-loaded with the most important context first.

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

Completeness3/5

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

For a 4-parameter tool with no annotations and no output schema, the description does an adequate job covering parameter semantics but leaves significant gaps. It doesn't explain what the export produces (file download? JSON data?), authentication requirements, error conditions, or practical usage examples. The purpose is clear but operational context is minimal.

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

Parameters4/5

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 documenting all 4 parameters with clear semantic meaning. It explains 'sid' as survey ID, 'period' as granularity level with allowed values, and both date parameters with format examples. The description adds significant value beyond the bare schema, though it doesn't explain parameter interactions or default behaviors for 'end_datetime'.

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 ('Export timeline') and target resource ('for a LimeSurvey survey'), making the purpose immediately understandable. It distinguishes itself from siblings like 'export_responses' or 'export_statistics' by focusing specifically on timeline data with aggregation. However, it doesn't explicitly contrast with these similar export tools, keeping it from a perfect score.

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 'export_responses' or 'export_statistics'. It mentions aggregation of submission counts but doesn't explain why one would choose timeline export over other export methods. There are no prerequisites, exclusions, or contextual recommendations provided.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/edgarrmondragon/limesurvey-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server