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update_data_table_row

Update a specific record in a Workato Data Table using its record ID, modifying only the fields provided. Ideal for changing existing row data without full replacement.

Instructions

Update a Data Table record by record_id (record API). Include only the fields to change in document_json ({field_name: value}). Get record_id from query_data_table ($record_id). MUTATES data — confirm first.

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>",
      "record_id": "<record_id>",
      "document_json": "<document_json>"
    },
    "name": "update_data_table_row"
  }
}

Input Schema

Name	Required	Description	Default
`table_id`	Yes
`record_id`	Yes
`document_json`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 31, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It explicitly warns 'MUTATES data — confirm first', disclosing the side effect. It also clarifies that only fields specified in document_json are changed, implying a merge rather than a full replacement. This is meaningful behavioral context beyond what the schema provides.

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 extremely concise—three short sentences that efficiently convey purpose, usage details, and a warning. Key information is front-loaded, with no filler 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 covers the core action, parameter usage for record_id and document_json, the mutation risk, and a prerequisite step (query_data_table). However, it omits the meaning of table_id and any information about return values or error behavior, which is notable given the absence of an output schema.

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

Parameters3/5

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

The description explains the purpose and format of document_json ('{field_name: value}') and instructs how to obtain record_id, but it does not clarify the meaning of table_id. With 0% schema coverage, this leaves a gap in parameter understanding for one of the three required parameters.

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 clearly states the action ('Update a Data Table record by record_id'), specifies the resource type (Data Table record), and the method (record API). It effectively distinguishes the tool from sibling tools like add_data_table_row, delete_data_table_row, and query_data_table.

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 clear context for usage: 'Get record_id from query_data_table' implies a prerequisite workflow and instructs to 'Include only the fields to change in document_json', indicating partial updates. It does not explicitly mention alternatives or when not to use it, but the contextual guidance is strong.

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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