Skip to main content
Glama

set_annotation

Set, update, or delete annotations for Figma nodes via Conduit MCP server. Supports single or batch operations and returns node-level status updates for efficient design management.

Instructions

Set, update, or delete annotation(s) for one or more Figma nodes.

Returns:

  • For single: { nodeId, updated/deleted }

  • For batch: Array<{ nodeId, updated/deleted }>

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entriesNoAn array of annotation operations to perform in batch. Optional.
entryNoA single annotation operation to perform. Optional.
Behavior4/5

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

Annotations provide rich behavioral information (readOnlyHint: false, edgeCaseWarnings about node not found errors and delete behavior, usage examples). The description adds value by specifying the return format for single vs batch operations, which isn't covered in annotations. No contradiction with annotations 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?

The description is extremely concise and well-structured: one sentence for the core functionality and a clear bullet-point format for return values. Every element serves a purpose with zero wasted words, making it easy to parse quickly.

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 the rich annotations (which cover safety, edge cases, and examples) and 100% schema coverage, the description provides adequate context. It adds the return format specification, which is helpful since there's no output schema. For a mutation tool with good annotation support, this is reasonably complete.

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?

With 100% schema description coverage, the schema fully documents the 'entries' and 'entry' parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without enhancing understanding of parameter usage or meaning.

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 action ('Set, update, or delete') and resource ('annotation(s) for one or more Figma nodes'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_annotation' or other annotation-related tools that might exist, though the 'set' vs 'get' distinction is implied.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_annotation' for retrieval or other annotation-related operations, nor does it specify prerequisites or appropriate contexts for setting vs updating vs deleting annotations.

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/amalinakurniasari/conduit'

If you have feedback or need assistance with the MCP directory API, please join our Discord server