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mcp-document-intelligence

mcp-document-intelligence

by AndreasDietzel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

analyze_document

Extracts text with OCR fallback, detects dates, references and keywords, and suggests an intelligent filename for a local document, with optional AI classification.

Instructions

Deep-analyze a single document: extract text (with OCR fallback), detect dates, references, keywords, and suggest an intelligent filename. Optionally uses Perplexity AI for classification.

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": {
      "filePath": "<filePath>"
    },
    "name": "analyze_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
filePathYesAbsolute path to document file

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.6.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the OCR fallback and an optional external AI dependency (Perplexity), which is real context an agent needs, but it never states whether the tool is read-only, whether the suggested filename is applied or only returned, or what happens if OCR or the AI call fails.

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?

Two tight sentences, front-loaded with the action and then the concrete outputs. Every clause conveys a distinct capability with no filler.

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?

With no output schema and no annotations, the description does the work of enumerating what comes back (text, dates, references, keywords, filename suggestion), which is the key gap it needs to fill. It falls short only on the mutation/read-only question and failure behavior.

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?

There is a single parameter (filePath) and schema description coverage is 100%, so the schema already documents it fully. The description adds nothing about path constraints, supported formats, or file-size limits, so the baseline 3 applies.

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 names a specific verb ('Deep-analyze') and resource ('a single document'), then enumerates the concrete outputs: extracted text with OCR fallback, dates, references, keywords, and a suggested filename. 'Single document' implicitly contrasts with the sibling analyze_folder, and 'suggest' distinguishes it from rename_document/smart_rename.

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?

Usage context is implied rather than stated: the singular-document scope and the filename suggestion hint at when to pick this over analyze_folder or smart_rename, but neither an explicit when-to-use statement nor any exclusion is given. The Perplexity AI option is mentioned without saying when it should be enabled.

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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