Skip to main content
Glama

extract_text

Extract text from images using local paths. Supports document parsing and card key-information extraction, with optional custom prompts for structured JSON output.

Instructions

提取图片中的文字,支持文档解析与卡证关键信息抽取。image_path: 本地图片路径;prompt: 可选的抽取指令(如要求 JSON 输出)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNo请提取图片中的全部文字
image_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It adds useful context about document parsing and JSON output capability via prompt, but lacks details on file format support, language limitations, or side effects. It does not indicate any destructive behavior, which is consistent with a read-only operation.

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 comprised of two concise sentences. The first front-loads the purpose, and the second explains parameters. There is no redundancy or filler, making it highly efficient and scannable.

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?

The tool has an output schema, so return values are communicated elsewhere. The description covers the core purpose and parameter semantics, but omits comparison with the sibling tool and potential limitations like file types or error conditions. Given the moderate complexity, it is sufficient but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero description coverage for parameters. The description compensates by explaining both parameters: 'image_path' as local path and 'prompt' as an optional instruction with an example (JSON output). This adds meaningful semantic value beyond the raw schema.

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's primary function—extracting text from images—with a specific verb and resource. It further specifies support for document parsing and card key-information extraction, which distinguishes it from the sibling tool 'describe_image'.

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?

The description implies the tool should be used for text extraction from images, but does not explicitly state when to use it versus 'describe_image' or provide exclusions. There is no guidance on alternative tools or when not to use this one.

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/1710782766/llm_vision'

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