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Raw blob bytes

fig_blob
Read-only

Get raw binary payloads from a Figma file's blob table by index to access vector geometry or glyph outlines, with base64/hex output and byte truncation.

Instructions

Return the raw bytes of one entry in the file's blob table. Vector geometry, glyph outlines and similar bulk payloads are stored there and referenced by index from fields such as vectorData.vectorNetworkBlob and Path.commandsBlob (fig_node reports these as vector.networkBlob / vector.fillBlobs). This server does not decode the vector-network format — you get the bytes, base64 or hex, truncated to maxBytes with a flag when longer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesPath to the .fig / .figma file (absolute, or relative to the server CWD).
indexYesBlob index, as reported by fig_node `vector.*`.
encodingNoDefault "base64".
maxBytesNoBytes to return before truncating (default 65536).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses truncation behavior ('truncated to maxBytes with a flag when longer'), output encodings (base64/hex), and the non-decoding limitation. These are meaningful behavioral traits that an agent needs to interpret results correctly.

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 sentences with zero fluff. The first sentence states the core operation, the second gives context about blob table contents and how indexes are referenced, and the third flags the format and truncation caveat. Every sentence earns its place.

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?

With no output schema, the description compensates by stating what the caller receives ('the bytes, base64 or hex, truncated to maxBytes with a flag when longer'). It covers parameter semantics and the key caveat (no decoding). Minor gaps exist around the exact response flag name and error cases for invalid indexes, but the description is adequate for a focused retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description adds real semantic value: it explains that `index` comes from fig_node's `vector.*` fields and what `maxBytes` does (truncation with a flag), grounding the parameters in actual usage. This goes beyond merely restating the schema.

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 opens with a precise verb and resource: 'Return the raw bytes of one entry in the file's blob table.' It further clarifies the payload types (vector geometry, glyph outlines) and explicitly states what the tool does not do ('does not decode the vector-network format'), making its unique role obvious and separating it from structural tools like fig_node.

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?

The description clearly conveys when to use the tool: whenever raw blob bytes are needed, with references to how indexes are obtained via fig_node fields. It also provides an explicit exclusion (no vector-network decoding), though it does not name a specific alternative tool for decoded output, leaving that to inference.

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