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mcp-document-intelligence

mcp-document-intelligence

by AndreasDietzel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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analyze_folder

Batch-analyze all documents in a folder to return metadata, filename suggestions, duplicate detection, and performance metrics, with optional file-type filters and recursive scanning.

Instructions

Batch-analyze all documents in a folder. Returns metadata, filename suggestions, duplicate detection, and performance metrics.

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": {
      "folderPath": "<folderPath>"
    },
    "name": "analyze_folder"
  }
}

Input Schema

NameRequiredDescriptionDefault
fileTypesNoFilter by extension (e.g. ['pdf','docx'])
recursiveNoInclude subdirectories (default: false)
folderPathYesAbsolute path to folder

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.6.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It helpfully enumerates what the call produces (metadata, filename suggestions, duplicate detection, performance metrics), which discloses the read-only analytical nature of the operation, but says nothing about cost/time on large folders, whether anything is modified, or how duplicate conflicts are reported.

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 sentences, zero filler, with the action front-loaded and the return payload following immediately. Every clause earns its place.

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, the description compensates by listing the return contents, and the three-parameter schema is fully documented. What remains thin is the absence of any guidance on large-folder behavior or the choice between this and its siblings.

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%, with folderPath, fileTypes, and recursive all documented in the schema itself, so the baseline is 3. The description adds no further meaning about parameters; 'all documents' is slightly at odds with the unused fileTypes filter but does not clarify it.

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?

Names a specific verb ('Batch-analyze') and resource ('all documents in a folder'), which implicitly contrasts with the single-document sibling analyze_document. It does not explicitly name that sibling, but an agent can still distinguish folder-level batch work from per-document work.

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 word 'Batch-analyze' implies when this tool applies (bulk folder processing rather than a single file), but the description never states when to prefer it over scan_directory, analyze_document, or smart_rename, and gives no prerequisites or exclusions. Usage is inferable rather than stated.

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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