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laveeshb

logicapps-mcp

by laveeshb

invoke_connector_operation

Invoke a dynamic operation on an API connection to retrieve resources (tables, queues, folders) or schemas for populating workflow action parameters.

Instructions

Invoke a dynamic operation on an API connection to fetch connection-specific data like dropdown values, schemas, or metadata. This is the equivalent of what the Logic Apps designer does when you click on a dropdown or text field - it calls the connector to populate the options.

WHEN TO USE THIS TOOL:

  • After getting connector swagger with get_connector_swagger, you see operations with 'x-ms-dynamic-values' or 'x-ms-dynamic-schema'

  • You need to list available tables, queues, folders, or other resources from a connection

  • You need to get the schema/columns for a specific table or entity

  • You're authoring a workflow and need to know valid values for action parameters

WORKFLOW FOR AUTHORING WITH CONNECTORS:

  1. Use get_connector_swagger to discover operations and see x-ms-dynamic-values annotations

  2. Use get_connections to find existing connections in the resource group

  3. Use invoke_connector_operation to call the dynamic operation (e.g., GetTables, GetQueues)

  4. Use the returned values to populate your workflow action parameters

COMMON EXAMPLES:

  • SQL: operationId='GetTables' returns list of tables, operationId='GetTable' with table parameter returns column schema

  • Service Bus: operationId='GetQueues' returns available queues

  • SharePoint: operationId='GetDataSets' returns sites, then GetTables for lists

  • Blob Storage: operationId='GetDataSets' returns containers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subscriptionIdYesAzure subscription ID
resourceGroupNameYesResource group containing the API connection
connectionNameYesName of the API connection (e.g., 'sql-1', 'servicebus', 'azureblob')
operationIdYesThe operationId from the connector swagger to invoke (e.g., 'GetTables', 'GetQueues', 'GetDataSets')
parametersNoParameters required by the operation. Check the swagger for required parameters. For example, GetTable requires {table: 'tableName'}
Behavior3/5

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

No annotations provided, so description carries full burden. It explains the tool invokes operations to fetch data, but does not state whether operations are read-only or have side effects, nor mention authentication or error behavior. Adequate but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections: purpose, when to use, workflow, examples. Front-loaded and informative, though slightly lengthy. Every sentence 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?

Given no output schema and no annotations, the description effectively explains purpose, usage, and common scenarios. Covers workflow integration and parameter behavior. Lacks details on return format and error handling, but sufficient for authoring context.

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?

Input schema covers all parameters with descriptions (100% coverage). Description adds value by mapping operationId to real examples (e.g., 'GetTables'), explaining the 'parameters' property as operation-specific, and contextualizing usage. Not just repeating schema.

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 tool invokes dynamic operations on API connections to fetch data like dropdown values, schemas, or metadata. It distinguishes itself from siblings like get_connector_swagger (which discovers operations) and get_connections (which finds connections).

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

Usage Guidelines5/5

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

Explicit 'WHEN TO USE THIS TOOL' section lists conditions: after getting swagger, to list resources, get schema, etc. Workflow steps show integration with other tools, and examples clarify use cases.

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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