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anuragfolio

figma-jev-console

by anuragfolio

jev_changelog

Retrieve Figma changes that require LLM coding, including layer codes, node IDs, code hints, and a ready prompt for implementation.

Instructions

Figma changes that need an LLM to finish in code (deletes, moves, reorders, expression-driven variants, anything the rules and Jev could not write), with layer codes, Figma node ids, code hints and a ready prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does explain what the changelog contains and what data accompanies each item, but it never explicitly states whether this is a read-only retrieval operation, whether it returns a list, or what side effects, if any, might occur.

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 a single dense sentence with no filler. The core subject is front-loaded, and the parenthetical list plus trailing data fields earn their place by conveying scope and expected content efficiently.

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

Completeness3/5

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

For a zero-parameter tool, the description covers the main data categories well, but it omits an explicit action verb and any guidance on how the returned changelog relates to sibling workflows. Since there is no output schema, the description should more clearly state what invoking the tool returns.

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?

The tool has zero parameters and the schema covers 100% of them, so the description does not need to explain parameter meaning. It adds value by describing the content fields that will come back, though that is more about output than parameters.

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 identifies the resource: Figma changes that need an LLM to finish in code, including specific categories and attached data. It does not use an explicit verb like 'list' or 'retrieve,' so it stops short of a fully specified action, but the intent is unambiguous from the name and content description.

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 such as jev_list_pending or jev_resolve_pending. The phrase 'anything the rules and Jev could not write' implies a boundary, but the description does not state when an agent should choose this changelog over sibling tools.

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