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Query workspace data

submit_query
Read-onlyIdempotent

Run SQL queries against Iceberg tables in a DPF workspace. Use SHOW TABLES to list available tables, then reference them by name to retrieve or analyze data.

Instructions

Run a SQL query against the Iceberg tables loaded into a workspace. Run SHOW TABLES to list the tables that actually exist. Reference tables by name only (no catalog/schema-qualified references).

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": {
      "sql": "<sql>"
    },
    "name": "submit_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYesSQL query, e.g. SELECT * FROM customers LIMIT 10
workspaceIdNoWorkspace to act on. Defaults to your only workspace if you have exactly one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description complements this by explaining the execution context: Iceberg tables in a workspace, and the constraint against schema-qualified names. It doesn't contradict annotations, and the brief directive to use SHOW TABLES adds a behavioral insight beyond the annotations.

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?

Three short sentences with zero fluff. The core action, a practical tip (SHOW TABLES), and a key constraint are packed into 35 words, all front-loaded. Every word contributes.

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?

For a simple read-only tool with two parameters and complete schema coverage, this description is sufficient. It covers what (SQL query), where (workspace Iceberg tables), and how (table-name only references). The only minor gap is an explicit statement of return format, but with no output schema and simple types, it's not necessary.

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 covers both parameters with descriptions, including an example for 'sql'. The tool description adds extra meaning by specifying that queries run against Iceberg tables in a workspace and that table names must not be schema-qualified, directly informing how to form the 'sql' parameter. This goes beyond the schema's baseline.

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 opens with a specific verb+resource: 'Run a SQL query against the Iceberg tables loaded into a workspace.' This clearly distinguishes it from sibling tools like 'run_data_job' or 'list_data' by pinpointing the Iceberg SQL querying context and the workspace scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides direct usage guidance: 'Run SHOW TABLES to list the tables that actually exist' and 'Reference tables by name only'. While it doesn't explicitly name alternatives, the instruction to use SHOW TABLES for discovery implies a workflow and sets expectations for table qualification, which is valuable for an agent.

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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