awslabs.document-loader-mcp-server

v1.0.16 safe
2.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for document parsing

🤖 AI Analysis

Final verdict: SAFE

The package appears safe with low risks across all categories reviewed. There are no indications of malicious activity or supply-chain attacks.

  • No network calls
  • Limited shell execution
  • Common obfuscation patterns
  • No credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: Shell execution is limited to fixed commands and without shell=True, reducing risk of arbitrary code execution.
  • Obfuscation: The observed pattern is commonly used for extending package paths and is not indicative of malicious obfuscation.
  • Credentials: No credential harvesting patterns were detected.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but there are no other suspicious flags.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_document_parsing.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://awslabs.github.io/mcp/servers/document-loader-mcp-se
  • Detailed PyPI description (5832 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 23 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 42 unique contributor(s) across 100 commits in awslabs/mcp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • amespace packages. __path__ = __import__('pkgutil').extend_path(__path__, __name__) # Copyright Amazon.com, In
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • , temp_dir, ] subprocess.run( cmd, check=True, stdout=subprocess.PIPE, stderr=sub
  • s used with fixed command, no shell=True import sys import tempfile from fastmcp import FastMCP from
  • B603 - fixed command with no shell=True, args are not user-controlled pdf_filename = Path(file_
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/mcp appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with awslabs.document-loader-mcp-server 
Create a document management system using the 'awslabs.document-loader-mcp-server' Python package. This system will allow users to upload various types of documents (PDFs, Word Docs, etc.) and automatically parse their contents into structured data. The goal is to make it easy for businesses to manage large volumes of documents by extracting key information such as names, dates, addresses, and other metadata.

### Features:
- **User Authentication:** Implement basic user authentication to ensure only authorized users can access and manage documents.
- **Document Upload:** Allow users to upload multiple types of documents (PDFs, DOCX, TXT).
- **Automatic Parsing:** Utilize the 'awslabs.document-loader-mcp-server' package to automatically parse uploaded documents into structured data.
- **Structured Data Storage:** Store parsed data in a database (e.g., PostgreSQL) for easy retrieval and analysis.
- **Search Functionality:** Enable users to search through stored documents based on keywords and metadata.
- **Document Version Control:** Keep track of different versions of documents and allow users to revert to previous versions if needed.
- **Reporting:** Generate reports based on the structured data extracted from documents, including summaries and statistics.

### Steps:
1. **Setup Environment:** Set up your development environment with Python, Flask (or Django), and PostgreSQL.
2. **Install Dependencies:** Install necessary packages including 'awslabs.document-loader-mcp-server', Flask (or Django), SQLAlchemy, and any other required libraries.
3. **Design Database Schema:** Design a schema to store document metadata and parsed content.
4. **Implement User Authentication:** Use Flask-Security or Django's built-in authentication to handle user registration, login, and logout.
5. **Document Upload Interface:** Create an interface where users can upload documents. Ensure the system supports multiple file formats.
6. **Integrate Document Loader:** Integrate 'awslabs.document-loader-mcp-server' to process uploaded documents and extract relevant information.
7. **Store Parsed Data:** Save the parsed data into the database for future reference.
8. **Develop Search Functionality:** Implement a search feature allowing users to find documents based on keywords and metadata.
9. **Version Control:** Add functionality to track document versions and allow users to revert to previous versions.
10. **Generate Reports:** Develop reporting tools that summarize the extracted data, providing insights into document content.
11. **Testing and Deployment:** Thoroughly test the application and deploy it to a cloud platform like AWS or Heroku.

This project will not only demonstrate the capabilities of the 'awslabs.document-loader-mcp-server' package but also provide a practical solution for managing and analyzing large document collections.

💬 Discussion Feed

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