Verbex Lab Tools

Reagent label parser

parse_reagent_label

Read-onlyIdempotent

Turn text read from a reagent bottle or kit label (OCR or typed) into structured fields such as product name, catalog number, lot, expiry, concentration and storage. Parses the provided text only; send the label text, not an image.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "parse_reagent_label"
  }
}

Input Schema

Name	Required	Description
`ocrText`	No	OCR text extracted from a reagent label image. The browser tool performs OCR locally; this endpoint parses provided text only.
`ocrConfidence`	No	Optional OCR confidence percentage from the OCR engine.
`sourceImageName`	No	Optional source image file name or identifier for review context.

Output Schema

Name	Required	Description
`card`	No	What the Verbex card draws; same values as the notebook entry.
`tool`	Yes
`result`	No
`humanUrl`	No
`warnings`	No
`safetyNotes`	No
`toolVersion`	No
`notebookEntry`	No	Markdown entry to paste into a lab notebook.
`validationErrors`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and non-destructive, so the safety profile is covered. The description adds genuinely useful context beyond that: this is a pure text-in/text-out transform with no image processing, which is the main behavioral surprise an agent could hit.

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?

Two sentences, both load-bearing: the first defines the transform and its output shape, the second kills the image-input misconception. Nothing is redundant or buried.

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?

An output schema exists, so return field details needn't be restated, and annotations cover safety. The definition is essentially complete for calling it, though it does not hint at whether ocrConfidence or sourceImageName affect results, a minor gap for a 3-parameter 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?

Schema description coverage is 100%, so ocrText, ocrConfidence and sourceImageName are already documented in the schema. The description only reinforces which parameter to send ("send the label text") and says nothing about how confidence or source image name are used. Baseline 3 is appropriate.

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?

States a specific verb (turn/parse) and a specific resource (reagent bottle or kit label text) and names the concrete output fields (product name, catalog number, lot, expiry, concentration, storage). An agent can immediately tell this is a text-to-structured-fields parser rather than an OCR or file-fetch tool.

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?

Explicitly bounds the input: "Parses the provided text only; send the label text, not an image." That exclusion prevents the most likely misuse. It stops short of naming a sibling (e.g., fetch) as the source for obtaining OCR text, so routing to an alternative is left implicit.

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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