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Superset MCP Integration

Superset MCP Integration

by aptro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

superset_saved_query_create

Create and save SQL queries for later reuse in Apache Superset by providing query data including database ID, schema, SQL text, and display name.

Instructions

Create a new saved query

Makes a request to the /api/v1/saved_query/ POST endpoint to save a SQL query for later reuse.

Args: query_data: Dictionary containing the query information including: - db_id: Database ID - schema: Schema name (optional) - sql: SQL query text - label: Display name for the saved query - description: Optional description of the query

Returns: A dictionary with the created saved query information including its ID

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": {
      "query_data": "<query_data>"
    },
    "name": "superset_saved_query_create"
  }
}

Input Schema

NameRequiredDescriptionDefault
query_dataYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
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 this is a POST endpoint (implying a write operation) and describes the return format, which adds value. However, it doesn't mention authentication requirements, error handling, rate limits, or whether the operation is idempotent, leaving behavioral gaps 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 well-structured with clear sections (purpose, args, returns) and uses bullet points for readability. It's appropriately sized but could be slightly more concise by integrating the purpose and endpoint details. Every sentence adds value, with no wasted words.

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 no annotations, no output schema, and a mutation tool with nested parameters, the description is moderately complete. It covers the purpose, parameter details, and return format, but lacks information on authentication, error cases, or sibling tool relationships. For a create operation, more behavioral context would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for the single parameter 'query_data', listing its dictionary contents (db_id, schema, sql, label, description) with notes on optional fields. This adds significant meaning beyond the bare schema, fully documenting the parameter structure.

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 creates a new saved query, specifying the verb 'create' and resource 'saved query'. It distinguishes from siblings like 'superset_saved_query_get_by_id' (read) and 'superset_sqllab_get_saved_queries' (list), but doesn't explicitly contrast them. The purpose is specific but lacks explicit sibling differentiation.

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?

No guidance on when to use this tool versus alternatives is provided. The description mentions saving SQL queries for later reuse, but doesn't specify prerequisites (e.g., authentication), when to use this over other query tools, or any constraints. Usage is implied but not explicitly 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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