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Superset MCP Integration

Superset MCP Integration

by aptro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

superset_sqllab_get_bootstrap_data

Retrieve SQL Lab configuration data including allowed databases and settings to initialize the SQL query interface in Apache Superset.

Instructions

Get the bootstrap data for SQL Lab

Makes a request to the /api/v1/sqllab/ endpoint to retrieve configuration data needed for the SQL Lab interface.

Returns: A dictionary with SQL Lab configuration including allowed databases and settings

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_sqllab_get_bootstrap_data"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:1328-1342 (handler)
    The main handler function for the 'superset_sqllab_get_bootstrap_data' tool. It is decorated with @mcp.tool() for registration and makes a GET request to Superset's /api/v1/sqllab/ endpoint to fetch bootstrap configuration data for SQL Lab.
    @mcp.tool()
@requires_auth
@handle_api_errors
async def superset_sqllab_get_bootstrap_data(ctx: Context) -> Dict[str, Any]:
    """
    Get the bootstrap data for SQL Lab

    Makes a request to the /api/v1/sqllab/ endpoint to retrieve configuration data
    needed for the SQL Lab interface.

    Returns:
        A dictionary with SQL Lab configuration including allowed databases and settings
    """
    return await make_api_request(ctx, "get", "/api/v1/sqllab/")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
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. It mentions the endpoint ('/api/v1/sqllab/') and return type ('A dictionary with SQL Lab configuration'), but fails to disclose critical behavioral traits: whether this requires authentication, rate limits, error handling, or if it's a read-only operation. For a tool with zero annotation coverage, this is insufficient, though it does add some context beyond the basic purpose.

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 front-loaded: the first sentence states the purpose, followed by endpoint details and return value. Every sentence adds value without redundancy, making it efficient and easy to parse. It appropriately balances brevity with necessary information.

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?

Given the tool's complexity (simple read operation with 0 params) and lack of annotations/output schema, the description is moderately complete. It explains what the tool does and what it returns, but misses behavioral details like authentication needs or error cases. Without an output schema, it should ideally describe the return structure more thoroughly, but the mention of 'dictionary with SQL Lab configuration' provides some context, making it minimally viable.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description correctly adds no parameter information, which is appropriate. Baseline for 0 params is 4, as it avoids unnecessary details and focuses on the tool's purpose and output.

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 the bootstrap data for SQL Lab' specifies the verb (get) and resource (bootstrap data for SQL Lab). It distinguishes from siblings like 'superset_sqllab_execute_query' or 'superset_sqllab_get_results' by focusing on configuration data rather than query execution or results. However, it doesn't explicitly contrast with all siblings, keeping it at 4 instead of 5.

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 stating it retrieves 'configuration data needed for the SQL Lab interface,' suggesting it should be used when setting up or initializing SQL Lab. However, it lacks explicit guidance on when to use this versus alternatives (e.g., other SQL Lab tools) or any prerequisites, such as authentication requirements. This makes it adequate but with gaps.

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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