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@guardbee/mcp-security-proxy

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document_scanner__scan_base64

Scan base64-encoded documents, attachments, or images for PII and credentials, with automatic type detection and optional OCR for textless files.

Instructions

Scan a document given as base64 (an attachment, an image a tool returned) for PII and credentials. The type is detected from the bytes.

Input Schema

NameRequiredDescriptionDefault
ocrNoOCR images and PDF pages without a text layer (default true; needs tesseract.js)
dataYesBase64 bytes (a data: URL prefix is allowed)
labelNo
includeNoOpt-in detectors: ip_address, mac_address, plate_tr
languagesNoTesseract languages, default ["tur", "eng"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.5
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Addedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It does usefully disclose that 'the type is detected from the bytes' (no format param needed) and scopes the detectors to PII and credentials, but says nothing about size limits, whether processing is local vs. remote (relevant since raw base64 data is being uploaded), or latency costs of OCR.

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?

Two tight sentences with the resource and the format constraint front-loaded and zero filler. Appropriately sized, though brevity comes at the cost of the missing behavioral and output detail noted elsewhere.

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?

There is no output schema, so the description is the only place an agent could learn what findings look like, and it says nothing about return shape, severity, or truncation behavior. For a 5-parameter scan tool with no annotations, that leaves a real gap despite an otherwise competent header sentence.

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 80%, so the schema already documents data, ocr, languages and include. The description adds only the framing that the payload is base64 bytes and that format detection is automatic, which explains the absence of a type parameter but does not add meaning to label or include.

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?

States a specific verb and resource ('Scan a document given as base64') plus the detection targets ('PII and credentials'), which sets it apart from generic scanners. It does not explicitly name its closest sibling, document_scanner__scan_document (file-path variant), though the base64 framing implies the difference.

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 parenthetical 'an attachment, an image a tool returned' hints at the input situations this is for, which is useful implicit guidance. However it never states when to choose this over document_scanner__scan_document or what to do if the input is a path, leaving routing to the agent's inference.

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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