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mcp-docparser

mcp-docparser

by Jasonzld
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

analyze_image

Analyze image content to detect text presence and retrieve metadata, enabling document parsing workflows to determine OCR needs and image details.

Instructions

Analyze image content, detect text presence, and get metadata

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": "analyze_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
image_pathYesPath to the image file

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.6/5.0
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 it discloses almost nothing: no mention of whether the operation is read-only, whether it invokes a costly vision model, what the metadata contains, or how text presence is reported. It only lists three capability categories.

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?

One short, front-loaded sentence with no wasted words. It is efficient, though the brevity comes at the cost of specificity rather than being tight-but-complete.

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

Completeness2/5

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

With no annotations and no output schema, the description is the only source of return-value information, yet it gives only three vague labels ('content', 'text presence', 'metadata') without structure, keys, or format. For a tool bundling three distinct capabilities, this leaves too much unspecified.

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% and the single image_path parameter is fully documented in the schema, so the baseline is 3. The description adds no path format, accepted file types, or relative-vs-absolute path guidance beyond what the schema already states.

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

Purpose3/5

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

The description names a general verb ('Analyze') plus three concrete outputs: content analysis, text presence detection, and metadata. This is more informative than a tautology, but 'analyze' is a vague catch-all and the definition does nothing to distinguish it from siblings like ocr_image, extract_text, or get_document_info, which appear to overlap on text extraction and metadata.

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 guidance on when to choose this tool over the many overlapping siblings (ocr_image, extract_text, batch_ocr). No prerequisites, no exclusions, no alternative named. The agent must guess from the name alone.

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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