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laveeshb

logicapps-mcp

by laveeshb

get_workflow_instructions

Provides step-by-step instructions for diagnosing failures, explaining, monitoring, creating, or fixing Azure Logic Apps workflows. Call this first for high-level workflow requests.

Instructions

Get step-by-step instructions for handling common user requests. CALL THIS FIRST when a user asks high-level questions like 'why is my workflow failing?', 'what does this workflow do?', or 'create a workflow'. Returns detailed sequences of tool calls to complete the task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesThe instruction topic: 'diagnose-failures' when user asks about failures or errors; 'explain-workflow' to understand what a workflow does; 'monitor-workflows' to check status and health; 'create-workflow' to build new workflows; 'fix-workflow' to modify and repair existing workflows
Behavior4/5

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

Without annotations, the description carries the full burden. It clearly states the output ('returns detailed sequences of tool calls') and its role as a first-step planning tool. No side effects are implied, and no contradictory information exists.

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, zero waste. The first sentence states purpose, the second provides usage guidance and output description. Every word is functional and necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description fully covers purpose, usage, and expected output. It is complete and leaves no ambiguity about what the tool does or when to invoke it.

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 coverage is 100%, so baseline is 3. The description's examples reinforce parameter usage but do not add new semantic details beyond what the schema already provides for each enum value.

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 returns step-by-step instructions for common user requests, with specific examples like 'why is my workflow failing?'. It distinguishes itself from siblings by instructing 'CALL THIS FIRST' and by focusing on high-level questions, contrasting with tools like get_troubleshooting_guide which likely address specific errors.

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?

Explicit guidance is given on when to use the tool ('CALL THIS FIRST when a user asks high-level questions...') with concrete examples. It lacks explicit 'when not to use' or alternative tool names, but the provided context is sufficiently directive for an agent.

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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