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Superset MCP Integration

Superset MCP Integration

by aptro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

superset_dataset_list

Retrieve datasets from Superset to view accessible data sources with paginated results for analysis and management.

Instructions

Get a list of datasets from Superset

Makes a request to the /api/v1/dataset/ endpoint to retrieve all datasets the current user has access to view. Results are paginated.

Returns: A dictionary containing dataset information including id, table_name, and database

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "superset_dataset_list"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:1091-1105 (handler)
    The handler function for the 'superset_dataset_list' tool. It is registered via @mcp.tool(), requires authentication, handles API errors, and makes a GET request to Superset's /api/v1/dataset/ endpoint to list available datasets.
    @mcp.tool()
@requires_auth
@handle_api_errors
async def superset_dataset_list(ctx: Context) -> Dict[str, Any]:
    """
    Get a list of datasets from Superset

    Makes a request to the /api/v1/dataset/ endpoint to retrieve all datasets
    the current user has access to view. Results are paginated.

    Returns:
        A dictionary containing dataset information including id, table_name, and database
    """
    return await make_api_request(ctx, "get", "/api/v1/dataset/")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it makes an API request to a specific endpoint, retrieves datasets based on user access permissions, and results are paginated. It also mentions the return format (dictionary with id, table_name, database). This covers authentication needs, response structure, and pagination behavior adequately for a read-only list operation.

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 well-structured and concise: first sentence states the purpose, second provides implementation details and access constraints, third notes pagination, and fourth describes the return format. Every sentence adds value without redundancy, and it's front-loaded with the core functionality.

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

Completeness4/5

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is fairly complete. It explains what the tool does, how it works (API endpoint), access constraints, pagination, and return structure. For a list tool with no complex inputs or outputs, this provides sufficient context, though it could mention error handling or example usage.

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?

The tool has 0 parameters, and schema description coverage is 100% (empty schema). The description doesn't need to add parameter semantics. A baseline of 4 is appropriate for zero-parameter tools, as there's no gap to compensate for.

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 tool's purpose: 'Get a list of datasets from Superset' (verb+resource). It distinguishes from siblings like superset_dataset_get_by_id (specific dataset) and superset_dataset_create (create vs list), but doesn't explicitly differentiate from other list tools like superset_chart_list or superset_dashboard_list. The purpose is specific and actionable.

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

Usage Guidelines3/5

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

The description implies usage context by mentioning 'the current user has access to view' and pagination, suggesting it's for browsing accessible datasets. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., superset_database_get_tables for underlying tables or superset_dataset_get_by_id for a specific dataset). Guidelines are implied but not explicit.

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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