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Superset MCP Integration

Superset MCP Integration

by aptro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

superset_database_update

Modify existing database connection properties in Apache Superset, including credentials, URIs, and configurations, through API requests.

Instructions

Update an existing database connection

Makes a request to the /api/v1/database/{id} PUT endpoint to update database connection properties.

Args: database_id: ID of the database to update data: Data to update, can include database_name, sqlalchemy_uri, password, and extra configs

Returns: A dictionary with the updated database information

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "database_id": "<database_id>",
      "data": "<data>"
    },
    "name": "superset_database_update"
  }
}

Input Schema

NameRequiredDescriptionDefault
database_idYes
dataYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API endpoint and that it updates properties, but doesn't cover critical aspects like authentication requirements, error handling, whether changes are reversible, rate limits, or what happens to unspecified properties. This is a significant gap for a mutation tool.

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 efficiently structured with a clear purpose statement, API endpoint detail, and separate Args/Returns sections. Every sentence adds value, though the API endpoint detail could be considered slightly technical for some users.

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 mutation tool with 2 parameters, no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral context, error information, and detailed output expectations. The absence of annotations increases the burden on the description, which it doesn't fully meet.

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 compensates well by explaining both parameters: 'database_id' is identified as the ID to update, and 'data' is described as including specific fields like database_name, sqlalchemy_uri, password, and extra configs. This adds meaningful context beyond the bare schema.

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 ('Update an existing database connection') and resource ('database connection properties'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like superset_database_create or superset_database_delete, which would be needed for 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. It doesn't mention prerequisites (e.g., needing an existing database ID), when not to use it, or how it differs from related tools like superset_database_create or superset_database_validate_parameters.

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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