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What converts automatically

supported_modules
Read-onlyIdempotent

Lists Make modules and Zapier steps that convert automatically into n8n nodes, letting you check whether a specific key converts before exporting.

Instructions

List the Make modules and Zapier steps that convert automatically, as a table of module key and the n8n node it becomes. Use it to answer "does X convert?" before the user exports anything; for one known key, suggest_replacement gives more detail. Modules not listed still convert to a placed, disabled n8n node with a suggestion.

Input Schema

NameRequiredDescriptionDefault
queryNoText to look for in the module key or the n8n node name, without regard to case, e.g. "sheets" or "slack". Leave out to list everything.
sourceNoOnly "make" or only "zapier". Leave out for both.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.2.0
    • addedInput schema / properties / query / description
      Added value: +"Text to look for in the module key or the n8n node name, without regard to case, e.g. \"sheets\" or \"slack\". Leave out to list everything."
    • addedInput schema / properties / source / description
      Added value: +"Only \"make\" or only \"zapier\". Leave out for both."
  2. First observedv0.1.1

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds a genuinely non-obvious behavioral fact: unlisted modules still convert into a placed but disabled n8n node with a suggestion, which prevents an agent from treating absence as a hard failure. Only minor gaps remain (no pagination/size expectations).

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 tight sentences, each earning its place: purpose plus output shape first, then the use case and the alternative, then the fallback caveat. Front-loaded and free of filler.

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?

No output schema exists, but the description compensates by specifying the returned table (module key and resulting n8n node) and the behavior for unlisted modules. For a zero-required-parameter read tool, an agent has everything needed to call and interpret 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 description coverage is 100%, so both the case-insensitive free-text query and the make/zapier enum are fully documented in the schema. The description adds no extra syntax or format guidance beyond that, so the baseline 3 applies.

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?

States a specific verb (List) and resource (Make modules / Zapier steps that convert automatically) and even describes the shape of the result as a module-key-to-n8n-node table. It is clearly distinguishable from siblings like convert_make and convert_zapier, which presumably perform conversions rather than report capability.

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?

Explicitly says when to use it (to answer "does X convert?" before the user exports anything) and names the alternative with the condition that selects it (for one known key, use suggest_replacement). Nothing is left to inference.

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