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Workato Dev MCP

Workato Dev MCP

by gmunro-lyft
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add_lookup_table_row

Append a row to a lookup table with a defined schema using a JSON object of column values. Use this to capture or log data.

Instructions

Append a row to a lookup table. entry_json is a JSON object string {column: value}. Table must have a defined schema. Useful for writing capture/log rows.

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>",
      "entry_json": "<entry_json>"
    },
    "name": "add_lookup_table_row"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_idYes
entry_jsonYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries the burden. It discloses that the table must have a defined schema and that entry_json is a JSON object string. However, it omits any details about permissions, return values, error behavior, or consequences of appending (e.g., duplicates).

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 the primary action front-loaded. Every sentence adds useful information without waste.

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 two-parameter append tool with no output schema, the description covers the essential aspects: purpose, required input format, a prerequisite (schema), and a typical use case. It lacks error or return details, but these are less critical for a straightforward operation.

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?

Schema description coverage is 0%, but the description adds meaning to entry_json by specifying it is a JSON object string {column: value}. table_id is not explained, though its name is fairly self-explanatory. Partially compensates for low coverage.

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?

Clearly states 'Append a row to a lookup table' with a specific verb and resource. Distinguishes from sibling tools like add_data_table_row by explicitly targeting lookup tables.

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?

Provides a clear use case: 'Useful for writing capture/log rows.' This gives context for when to use the tool, though it does not explicitly mention alternatives like add_data_table_row.

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

Deploy Server

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