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Superset MCP Integration

Superset MCP Integration

by aptro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

superset_query_stop

Terminate a running query in Apache Superset by providing the client ID, preventing unnecessary resource consumption and allowing query management.

Instructions

Stop a running query

Makes a request to the /api/v1/query/stop endpoint to terminate a query that is currently running.

Args: client_id: Client ID of the query to stop

Returns: A dictionary with confirmation of query termination

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": {
      "client_id": "<client_id>"
    },
    "name": "superset_query_stop"
  }
}

Input Schema

NameRequiredDescriptionDefault
client_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
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 states the action ('terminate a query') and mentions the API endpoint, but lacks critical details like required permissions, whether this is a destructive operation, error handling, or rate limits. This is inadequate for a mutation tool with zero 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.

Conciseness4/5

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

The description is well-structured and appropriately sized, with a clear title-like statement followed by brief implementation details and parameter/return explanations. Every sentence adds value, though the API endpoint detail could be considered slightly extraneous for an agent's decision-making.

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 this is a mutation tool with no annotations, no output schema, and low schema description coverage, the description is incomplete. It lacks information about side effects, error cases, authentication requirements, and what the return dictionary contains, making it insufficient for safe and effective use.

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 description adds meaningful context for the single parameter ('client_id: Client ID of the query to stop'), which is valuable since schema description coverage is 0%. It clarifies what the parameter represents, though it doesn't specify format or sourcing details. For a tool with only one parameter, this provides sufficient semantic understanding.

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 with a specific verb ('stop') and resource ('a running query'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'superset_sqllab_execute_query' or 'superset_query_get_by_id', which prevents 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 a running query first) or compare it to other query-related tools in the sibling list, leaving the agent to infer usage context.

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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