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Gydmation

by io.github.hacrootechnologies
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AI workflow patterns

get_ai_workflow_patterns
Read-only
Architectures for AI workflows in n8n with how to wire them and common
pitfalls. Patterns: ai_agent_with_tools, rag_knowledge_base,
classify_and_route, summarize_and_notify, human_in_the_loop.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_ai_workflow_patterns"
  }
}

Input Schema

NameRequiredDescriptionDefault
patternNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety behavior is covered. The description adds useful context about architecture examples and pitfalls, but it does not explain the effect of omitting the pattern parameter or what the response structure looks like.

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 two sentences with no filler, front-loading the resource and then listing the supported patterns. Every phrase contributes useful information for selecting and invoking the tool.

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 read-only lookup tool with one optional parameter and no output schema, the description provides enough context about the content and supported patterns. The main omission is what happens when the pattern parameter is omitted or empty, which would be helpful given the default value.

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%, so the description must compensate for the undocumented pattern parameter. It does list the valid pattern names, which is valuable, but it does not explain that an empty default likely returns all patterns or how exact matching works, leaving a semantic gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource is AI workflow patterns in n8n and specifies the content includes architectures, wiring, and common pitfalls. It lists concrete pattern names, making the purpose concrete, but it does not differentiate itself from sibling tools such as get_best_practices or generate_workflow.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus alternatives, and no prerequisites or exclusions are stated. The pattern list helps identify supported values but does not tell an agent under what circumstances to call this tool rather than a sibling.

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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