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FreezeText MCP

FreezeText MCP

by juergenkoller-software
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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ocr_image

Extract text from a base64-encoded PNG or JPEG image using Apple Vision OCR.

Instructions

Runs OCR on a provided base64-encoded image (PNG or JPEG) and returns the recognized text.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image": "<image>"
    },
    "name": "ocr_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
imageYesBase64-encoded PNG or JPEG image data

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It only states the basic operation. Important aspects like error handling, image size limits, processing location (local/remote), or required permissions are omitted, leaving the agent without transparency.

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 a single, concise sentence. It front-loads key information: action, input format, and output. There is no wasted text.

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?

For a simple tool with one parameter and no output schema, the description is mostly complete. It specifies input and output clearly. However, lacking an output schema, it could briefly describe the return format (e.g., string or structured), but the current 'returns the recognized text' is adequate.

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 coverage is 100% for the single 'image' parameter, with a description matching the tool's description. The tool description reinforces that the image must be base64-encoded and in PNG/JPEG format, but adds minimal new meaning beyond the 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 action: runs OCR on a base64-encoded image. It specifies supported formats (PNG or JPEG) and outputs recognized text, distinguishing it from sibling tools like capture_region or set_ocr_languages.

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

Usage Guidelines2/5

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

The description does not provide any guidance on when to use this tool versus alternatives. For example, it doesn't mention that the image must already be captured or that this tool complements capture tools. No exclusions or context for its use.

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

Deploy Server

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