Skip to main content
Glama
Sign In
ocr-mcp

ocr-mcp

by Prekzursil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

compare_engines

Run all OCR engines on one image without ground truth. Get per-engine text, confidence, agreement, and consensus engine.

Instructions

Run ALL available engines on one image and compare them — the core accuracy tool when you have no ground truth.

Returns JSON: per-engine {text, mean_confidence, ok}, plus pairwise text similarity, average agreement, and a 'consensus_engine' (the one whose output best agrees with the others).

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": {
      "path": "<path>"
    },
    "name": "compare_engines"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
langNoen

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description bears full burden. It discloses the return JSON structure in detail, including per-engine fields and consensus engine. It does not explicitly state read-only or destructive nature, but it's implied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: first defines purpose, second details output. No unnecessary text. Slightly more could be added for parameter context, but it remains efficient.

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?

Given the presence of an output schema (not shown), the description explains return values well. It does not cover error cases or prerequisites (e.g., engines must be available). Siblings provide context, but missing parameter info reduces completeness.

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

Parameters2/5

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

Schema coverage is 0%, but the description adds no information about parameters. It does not explain 'path' or 'lang' beyond what the schema provides (name, type, required). This is a missed opportunity to clarify what image path is expected or language usage.

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 uses specific verb-resource ('Run ALL available engines on one image and compare') and distinguishes from siblings like batch_ocr and ocr_image. It clearly states it's the core accuracy tool when no ground truth exists.

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?

The description explicitly states when to use ('when you have no ground truth'), implying contrast with evaluate_accuracy. However, it does not explicitly mention when not to use or list alternatives beyond the inferred context.

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

Deploy Server

Other Tools