Skip to main content
Glama
devrobotlabs

@devrobotlabs/visionapi-mcp

Official
by devrobotlabs

Get a Vision API preset

vision_get_preset
Read-onlyIdempotent

Get the authoritative field schema for any preset, with each field's type, description, and whether it's always present, so you can parse responses correctly.

Instructions

The full field list for one preset: every field name, its type, its description, and whether it is always present.

Free — no credits, no API key needed.

This is the ONLY correct source for a preset's field names. Never write one from memory: the catalogue is versioned and a name you remember from another project may not exist here, and a schema built on a guessed name fails at the point where you are parsing the response rather than at the point where you made it up.

Fields marked ★ are present on every response; the rest appear only when the document carries them. A field holding null means the document did not have it, never that the call failed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesA preset name from vision_list_presets, e.g. "invoice".
formatNomarkdown (default) — compact, readable, absent fields summarised rather than repeated. compact_json — the same information as data, with _not_found and _low_confidence arrays, for when you will parse it. json — the API response verbatim; use it when you are writing HTTP code against the contract.markdown

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  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?

Annotations already mark the tool as read-only and idempotent, but the description adds critical behavioral semantics: fields marked ★ are always present, others appear only when the document carries them, and null means absence rather than failure. This goes beyond the annotation hints and clarifies output interpretation.

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 well-organized, with the purpose front-loaded and each paragraph adding distinct value: field list definition, free access, correctness warning, and interpretation rules. It is concise, with zero fluff, and every sentence earns its place.

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?

Even without an output schema, the description fully explains what the response will contain (field names, types, descriptions, presence markers), the meaning of null, and how the format parameter affects the output. It is complete for a simple read-only getter tool.

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?

The input schema provides 100% description coverage for both parameters (name and format), including detailed enum descriptions. The tool description adds no new parameter information beyond what the schema already supplies, so the baseline score of 3 is justified.

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 clearly states the tool fetches the full field list for one preset, with every field name, type, description, and presence marker. It explicitly calls itself the 'ONLY correct source' for a preset's field names, distinguishing it from sibling tools like vision_list_presets and from guessing.

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?

The description tells the agent when to use this tool (to obtain authoritative field names) and when not to (never write from memory), while also noting it is free and requires no API key. It explains how to interpret the response (★ markers, null meaning) and warns against using guessed names.

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

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/devrobotlabs/visionapi-mcp'

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