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aptro

Superset MCP Integration

by aptro
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

superset_database_get_related_objects

Retrieve charts and dashboards linked to a specific database in Apache Superset to identify dependencies and assess impact before modifications.

Instructions

Get charts and dashboards associated with a database

Makes a request to the /api/v1/database/{id}/related_objects/ endpoint to retrieve counts and references of charts and dashboards that depend on this database.

Args: database_id: ID of the database

Returns: A dictionary with counts and lists of related charts and dashboards

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYes

Implementation Reference

  • main.py:1017-1038 (handler)
    Handler function implementing the 'superset_database_get_related_objects' tool. It makes a GET request to Superset's /api/v1/database/{database_id}/related_objects/ endpoint to retrieve charts and dashboards associated with the specified database.
    @mcp.tool()
@requires_auth
@handle_api_errors
async def superset_database_get_related_objects(
    ctx: Context, database_id: int
) -> Dict[str, Any]:
    """
    Get charts and dashboards associated with a database

    Makes a request to the /api/v1/database/{id}/related_objects/ endpoint to retrieve
    counts and references of charts and dashboards that depend on this database.

    Args:
        database_id: ID of the database

    Returns:
        A dictionary with counts and lists of related charts and dashboards
    """
    return await make_api_request(
        ctx, "get", f"/api/v1/database/{database_id}/related_objects/"
    )

Tool Definition Quality

A4/5.0
Behavior3/5

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 that the tool makes an API request and retrieves counts and references, which is helpful behavioral context. However, it doesn't mention whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions, leaving gaps in behavioral understanding for a tool with no 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized with four sentences: purpose statement, implementation detail, parameter explanation, and return value description. Each sentence adds value without redundancy, and the information is front-loaded with the core purpose first.

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 moderate complexity (single parameter, read-focused operation), no annotations, and no output schema, the description does a reasonably complete job. It explains what the tool does, the parameter meaning, and the return structure. However, it could be more complete by mentioning authentication needs or error handling, which are important for API tools.

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 schema description coverage is 0%, so the description must compensate. It clearly explains that 'database_id' is the 'ID of the database', adding essential meaning beyond the schema's basic type information. Since there's only one parameter, this provides adequate semantic context, though it doesn't specify format constraints or examples.

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

Purpose5/5

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

The description clearly states the tool's purpose with specific verbs ('Get charts and dashboards') and resources ('associated with a database'), and distinguishes it from siblings like superset_database_get_by_id or superset_database_list by focusing on related objects rather than database properties. The first sentence directly answers what the tool does.

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 when you need to find dependencies of a database, but doesn't explicitly state when to use this tool versus alternatives like superset_database_get_by_id for database details or superset_chart_list for all charts. No explicit exclusions or prerequisites are mentioned, leaving usage context somewhat implied rather than clearly defined.

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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