Skip to main content
Glama

read_text

Extract printed text or seven-segment display digits from a camera frame or named region via OCR, returning the recognized text and a confidence score.

Instructions

Perform OCR to extract printed text or seven-segment display digits from the camera frame or region. Returns extracted text and confidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cameraNoCamera ID
regionNoOptional named region containing text/display

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose the output shape ("extracted text and confidence") and that the source can be a full frame or a named region, but omits whether the camera must be streaming, error/failure behavior when no text is found, or any auth/resource constraints.

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 tight sentences, front-loaded with the action and followed by the return value. No filler or redundancy.

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?

There is no output schema, so the description correctly summarizes the return (text + confidence). For a two-parameter, zero-required tool this is nearly sufficient; the only real gap is the absence of any precondition or failure-mode context, which is minor given the tool's simplicity.

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% with only two simple parameters, so the schema documents both. The phrase "camera frame or region" mildly reinforces how the two params relate (camera plus optional region), but adds no format or default detail beyond the schema. Baseline 3 applies.

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+resource ("Perform OCR to extract printed text or seven-segment display digits") and names the input surface ("camera frame or region"). No sibling tool does OCR, so the agent can route to it unambiguously.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the purpose: this is the tool for reading text from a camera view. However, it never states when to use it versus siblings like capture_image, measure, or get_timeline, and gives no preconditions (e.g., whether a frame must be captured first).

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