superset-mcp

estimate_query_cost

Estimate SQL query execution costs for supported databases to optimize performance and resource allocation.

Instructions

Estimate the cost of running a SQL query (supported databases only).

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>",
      "sql": "<sql>"
    },
    "name": "estimate_query_cost"
  }
}

Input Schema

Name	Required	Description	Default
`database_id`	Yes
`sql`	Yes
`schema`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Apr 14, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'supported databases only', adding some context about limitations, but fails to disclose critical behavioral traits such as whether this is a read-only operation, if it requires specific permissions, potential rate limits, or what the output entails (e.g., cost units, accuracy). For a tool with no annotations, 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.

Conciseness5/5

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

The description is a single, efficient sentence with zero waste—it directly states the tool's function and a key limitation. It is appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.

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 the tool's complexity (cost estimation with 3 parameters) and no annotations, the description is incomplete—it lacks details on parameters, behavioral traits, and usage context. However, the presence of an output schema mitigates some need to explain return values. Overall, it's minimally adequate but has clear gaps.

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 description must compensate by explaining parameters. It only implies 'SQL query' and 'databases' without detailing the three parameters (database_id, sql, schema). No meaning is added beyond what the schema names suggest, leaving parameters largely undocumented and unclear in usage.

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 verb ('estimate') and resource ('cost of running a SQL query'), making the purpose specific and understandable. It distinguishes from siblings like execute_sql (which runs queries) by focusing on cost estimation rather than execution. However, it doesn't fully differentiate from all siblings (e.g., format_sql also processes SQL).

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 minimal guidance with 'supported databases only', which hints at limitations but doesn't specify when to use this tool versus alternatives like execute_sql for actual query execution. No explicit when/when-not scenarios or prerequisite checks are mentioned, leaving usage context vague.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.