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Superset MCP Integration

by aptro

superset_database_validate_parameters

Validate database connection parameters before establishing connections in Apache Superset to ensure configuration accuracy and prevent connection failures.

Instructions

Validate database connection parameters

Makes a request to the /api/v1/database/validate_parameters/ endpoint to verify if the provided connection parameters are valid without creating a connection.

Args: parameters: Connection parameters to validate

Returns: A dictionary with validation results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parametersYes

Implementation Reference

  • The handler function decorated with @mcp.tool() that implements the superset_database_validate_parameters tool. It validates database connection parameters by posting to Superset's API endpoint /api/v1/database/validate_parameters/ using the make_api_request helper.
    @mcp.tool()
    @requires_auth
    @handle_api_errors
    async def superset_database_validate_parameters(
        ctx: Context, parameters: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Validate database connection parameters
    
        Makes a request to the /api/v1/database/validate_parameters/ endpoint to verify
        if the provided connection parameters are valid without creating a connection.
    
        Args:
            parameters: Connection parameters to validate
    
        Returns:
            A dictionary with validation results
        """
        return await make_api_request(
            ctx, "post", "/api/v1/database/validate_parameters/", data=parameters
        )
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the tool makes an API request and validates parameters without creating a connection, which gives some behavioral context. However, it lacks details on authentication requirements, rate limits, error handling, or what specific validation checks are performed. For a validation tool with zero annotation coverage, this is insufficient.

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 sized and front-loaded: the first sentence states the purpose clearly, followed by additional context. It uses three sentences total with no wasted words. However, the structure could be slightly improved by integrating the Args and Returns sections more seamlessly, but it's still efficient.

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?

Given the tool's complexity (validation operation with nested object parameter), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't detail the parameter structure, validation criteria, or return format beyond 'a dictionary with validation results.' For a tool that could involve sensitive connection data, more context is needed.

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 schema provides no parameter details. The description adds minimal value: it states 'parameters: Connection parameters to validate' but doesn't explain what these parameters are (e.g., host, port, credentials), their format, or constraints. This fails to compensate for the coverage gap, leaving the single parameter largely undocumented.

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: 'Validate database connection parameters' and specifies it 'makes a request to the /api/v1/database/validate_parameters/ endpoint to verify if the provided connection parameters are valid without creating a connection.' This is specific (verb+resource+scope) and distinguishes it from sibling tools like superset_database_test_connection or superset_database_create. However, it doesn't explicitly differentiate from all siblings, so it's not a 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 validates parameters 'without creating a connection,' which suggests it's for pre-checking before actual database operations. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like superset_database_test_connection or superset_database_create, nor does it mention prerequisites or exclusions. This is typical implied usage.

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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