mcp-workato

get_recipe

Fetch a single recipe's full details by ID: name, description, DSL code, connections, trigger, status, job counts, and version. Optionally include tags.

Instructions

Get full details of a single recipe by id (GET /api/recipes/:id). Returns name, description, code (recipe DSL), config (connections), trigger, running status, job counts, tags, version info, etc.

Pass includes=['tags'] to also retrieve the recipe's tag handles.

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": {
      "id": "<id>"
    },
    "name": "get_recipe"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Recipe numeric id, e.g. 12345
`includes`	No	Additional fields to include. Accepted value: ['tags']

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 2, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full disclosure burden. It reveals the tool's behavior by listing the returned fields (name, description, code, config, trigger, status, job counts, tags, version info) and explains the includes parameter's effect. It does not cover error handling or auth, but for a read-only GET this is reasonable.

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?

Two sentences, dense with useful information. The main purpose is front-loaded, and the optional parameter tip is separated clearly. No wasted words.

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 single-resource fetch, the description is nearly complete: it states the resource identifier, the expected output fields, and the optional include behavior. Missing details like error cases or authentication are minor for this simple GET tool. The returned field list partially compensates for 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?

Schema description coverage is 100%, so the baseline is 3. The schema already explains both id and includes, and the description reinforces the includes usage without adding significantly new meaning beyond what the schema provides.

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 ('Get full details') and a specific resource ('a single recipe by id'), and explicitly names the endpoint. It distinguishes itself from sibling list tools like list_recipes by emphasizing 'single recipe', so an agent can clearly understand its 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?

The description provides clear context: use this to retrieve full details for one recipe by id, versus listing or searching. It does not explicitly name alternatives or exclusion conditions, but the 'single recipe' phrasing and the inclusion of the includes parameter give enough guidance for correct selection.

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