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query_data_table

Fetch specific records from a Workato Data Table by applying filters, selecting fields, ordering, and paginating results.

Instructions

Query rows in a Workato Data Table (its record API, on a separate host). table_id is the table UUID from list_data_tables. Optional where_json is a JSON filter like {"Status":{"$eq":"Approved"}} (operators: $eq,$ne,$gt,$gte,$lt,$lte,$in,$starts_with,$and); select_json is a JSON array of field names; limit defaults to 20; order is a field name. Returns rows as name->value dicts including $record_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": {
      "table_id": "<table_id>"
    },
    "name": "query_data_table"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`order`	No
`table_id`	Yes
`where_json`	No
`select_json`	No
`continuation_token`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 31, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses key behaviors: supported where_json operators, default limit, ordering by field, and the return format as name->value dicts with $record_id. It misses continuation_token semantics and pagination behavior, but this is a minor omission for a query tool.

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 three dense sentences that front-load the purpose and then efficiently cover parameter semantics and return format. Every sentence adds valuable information with no fluff or redundancy.

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?

The description handles the lack of an output schema by explicitly stating the return format. It covers the core query functionality well but leaves continuation_token and pagination unexplained, which is a real gap for a tool with a limit parameter. Overall, it is more complete than most tool definitions.

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 explains five of six parameters in detail (where_json operators, select_json format, limit default, order field, table_id source), which is essential given 0% schema coverage. The only gap is continuation_token, which is not mentioned at all, leaving the agent to infer its purpose from the name.

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 states a specific verb and resource: 'Query rows in a Workato Data Table (its record API, on a separate host).' This clearly distinguishes it from sibling tools like query_lookup_table and the data table mutation tools. The mention of 'record API' and 'separate host' adds important scoping.

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?

The description provides useful context by explaining that table_id comes from list_data_tables and demonstrates how to construct where_json. However, it does not explicitly exclude alternatives or state 'when not to use' this tool, so it stops short of full guidance.

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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