Skip to main content
Glama

ocr_image

Extract text from images via OCR. Supports file paths, URLs, data URIs, or base64 strings; multiple languages with Tesseract.

Instructions

Extract readable text from an image (OCR).

Accepts a file path, URL, data URI, or base64 string. Uses local Tesseract when installed; otherwise falls back to the configured vision provider. lang is a Tesseract language code (e.g. 'eng', 'chi_sim', 'eng+chi_sim').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoeng
imageYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description discloses input formats (file path, URL, data URI, base64) and backend behavior (local Tesseract or vision provider fallback). This adds behavioral context beyond the schema.

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 concise sentences with no redundancy. Purpose is front-loaded, and every sentence adds necessary information.

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?

Given 2 parameters, no annotations, and existence of an output schema, the description covers input, language, and backend behavior adequately. Output format not required due to output schema.

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?

Schema coverage is 0%, but the description fully explains both parameters: image accepts multiple formats, and lang is a Tesseract code with examples, adding crucial meaning beyond type strings.

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?

Description clearly states 'Extract readable text from an image (OCR)', specifying the verb, resource, and technique. It distinguishes from siblings like analyze_image and web_search.

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 but not explicitly stated. No guidance on when to use vs. alternatives like analyze_image, nor when not to use this tool.

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

Install Server

Other Tools

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/lijiatuk/dstools'

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