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mcp-docparser

mcp-docparser

by Jasonzld
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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ocr_image

Extract text from images via OCR, supporting Chinese and English. Specify the image path; optionally set the language.

Instructions

Extract text from image using OCR (supports Chinese and English)

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_path": "<image_path>"
    },
    "name": "ocr_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
languageNoOCR language (chi_sim, eng, or chi_sim+eng)chi_sim+eng
image_pathYesPath to the image file

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden and does not meet it. It does not state that the operation is read-only/non-mutating, what happens when no text is found, whether results are plain text or structured, or any error behavior.

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?

A single front-loaded sentence with the verb and resource first and no filler. The parenthetical language note is compact, though it duplicates information already in the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter read tool with no output schema and full schema description coverage, the description is minimally adequate. It omits return format and failure/timing behavior, and gives no routing guidance among the several overlapping sibling tools.

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%, so both parameters (image_path, language) are already documented with defaults and allowed values. The description echoes the language capability (Chinese and English) but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource (extract text from an image) and the mechanism (OCR), which is clear. It does not differentiate from siblings such as extract_text, batch_ocr, or analyze_image, so an agent cannot tell from the text alone which of these to pick.

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?

There is no when-to-use guidance and no mention of alternatives, despite siblings like batch_ocr (multiple images) and extract_text (possibly non-image sources) that overlap directly. The only usage hint is the implied 'single image' scope from the name and the language support note.

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

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