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@devrobotlabs/visionapi-mcp

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by devrobotlabs

List Vision API presets

vision_list_presets
Read-onlyIdempotent

Retrieve the full catalogue of document presets, each with schema fields and purpose. Identify supported document types and preset names for coding. Free, no API key needed.

Instructions

List the preset catalogue: every named schema, with its field count and what it is for.

Free — no credits, no API key needed.

Read this when the user asks what document types are supported, or when you need a preset name to put in code. Do NOT call it to pick a preset before an extraction: preset:"auto" classifies the file server-side for free and is better at it than matching a description by eye, and vision_detect costs a fifth of a credit if you want the ranking. Listing 28 presets to guess one is the expensive path to a worse answer.

The names here are catalogue entries, not field names. Call vision_get_preset before writing any field name into code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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 declare readOnly, openWorld, and idempotent, but the description adds valuable context: it's free with no credits or API key, and the names are catalogue entries not field names (cautioning against misuse). No contradictions with annotations. This adds meaningful behavioral safety and cost information beyond the structured metadata.

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-structured: it opens with what the tool does, then cost, then usage and anti-usage, then a caution. Every sentence earns its place and adds distinct value. It's compact yet comprehensive, avoiding fluff while covering all necessary aspects.

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?

The description is complete for a simple listing tool with good annotations. It explains the purpose, cost, usage contexts, and a critical caution (catalogue entries vs field names). Without an output schema, it gives enough about the content (field count and purpose). The combination of annotations and description offers a full picture for an agent to use it correctly.

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 schema covers 100% of the parameter (format) with a detailed description of each enum value. The tool description does not add extra parameter detail, but since schema coverage is complete, the baseline of 3 is appropriate. The description doesn't need to repeat schema info; it focuses on when to use the tool, which is fine.

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 it lists the preset catalogue with field count and purpose. It distinguishes itself from siblings by specifying exactly what it provides (catalogue entries) and explicitly contrasts with vision_get_preset (which retrieves a single preset) and other tools. This is a specific verb+resource with clear scope.

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 gives explicit when-to-use scenarios (user asks about document types, needs preset name for code) and when-not-to-use scenarios (avoid for preset selection before extraction, with alternatives like preset:'auto' and vision_detect). It names specific alternative tools and explains why they are better in those situations. This is exemplary usage guidance.

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

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