Which integrations are available for this server?

Integrated for numerical computing to support document analysis calculations, as identified in the dependencies section. Utilized for data manipulation and analysis of document collections, allowing structured processing of text data. Integrated for full type validation and data modeling within the FastMCP framework. Core runtime environment for the FastMCP Document Analyzer server, supporting Python 3.8+ as specified in the requirements. Used for machine learning utilities, particularly TF-IDF vectorization and cosine similarity for document search functionality.

How do I use FastMCP Document Analyzer?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@FastMCP Document Analyzer analyze the sentiment of this customer review" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🔍 FastMCP Document Analyzer

A comprehensive document analysis server built with the modern FastMCP framework

Python FastMCP License

📋 Table of Contents

Related MCP server: Textin MCP Server

🌟 Features

📖 Document Analysis

🎭 Sentiment Analysis: VADER + TextBlob dual-engine sentiment classification
🔑 Keyword Extraction: TF-IDF and frequency-based keyword identification
📚 Readability Scoring: Multiple metrics (Flesch, Flesch-Kincaid, ARI)
📊 Text Statistics: Word count, sentences, paragraphs, and more

🗂️ Document Management

💾 Persistent Storage: JSON-based document collection with metadata
🔍 Smart Search: TF-IDF semantic similarity search
🏷️ Tag System: Category and tag-based organization
📈 Collection Insights: Comprehensive statistics and analytics

🚀 FastMCP Advantages

⚡ Simple Setup: 90% less boilerplate than standard MCP
🔒 Type Safety: Full type validation with Pydantic
🎯 Modern API: Decorator-based tool definitions
🌐 Multi-Transport: STDIO, HTTP, and SSE support

🚀 Quick Start

1. Clone and Setup

git clone <repository-url>
cd document-analyzer
python -m venv venv
source venv/Scripts/activate  # Windows
# source venv/bin/activate    # macOS/Linux

2. Install Dependencies

pip install -r requirements.txt

3. Initialize NLTK Data

python -c "import nltk; nltk.download('punkt'); nltk.download('vader_lexicon'); nltk.download('stopwords'); nltk.download('punkt_tab')"

4. Run the Server

python fastmcp_document_analyzer.py

5. Test Everything

python test_fastmcp_analyzer.py

📦 Installation

System Requirements

Python 3.8 or higher
500MB free disk space
Internet connection (for initial NLTK data download)

Dependencies

fastmcp>=2.3.0      # Modern MCP framework
textblob>=0.17.1    # Sentiment analysis
nltk>=3.8.1         # Natural language processing
textstat>=0.7.3     # Readability metrics
scikit-learn>=1.3.0 # Machine learning utilities
numpy>=1.24.0       # Numerical computing
pandas>=2.0.0       # Data manipulation
python-dateutil>=2.8.2  # Date handling

Optional: Virtual Environment

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Activate (macOS/Linux)
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🔧 Usage

Starting the Server

Default (STDIO Transport)

python fastmcp_document_analyzer.py

HTTP Transport (for web services)

python fastmcp_document_analyzer.py --transport http --port 9000

With Custom Host

python fastmcp_document_analyzer.py --transport http --host 0.0.0.0 --port 8080

Basic Usage Examples

# Analyze a document
result = analyze_document("doc_001")
print(f"Sentiment: {result['sentiment_analysis']['overall_sentiment']}")

# Extract keywords
keywords = extract_keywords("Artificial intelligence is transforming healthcare", 5)
print([kw['keyword'] for kw in keywords])

# Search documents
results = search_documents("machine learning", 3)
print(f"Found {len(results)} relevant documents")

# Get collection statistics
stats = get_collection_stats()
print(f"Total documents: {stats['total_documents']}")

🛠️ Available Tools

Core Analysis Tools

Tool	Description	Example
`analyze_document`	🔍 Complete document analysis	`analyze_document("doc_001")`
`get_sentiment`	😊 Sentiment analysis	`get_sentiment("I love this!")`
`extract_keywords`	🔑 Keyword extraction	`extract_keywords(text, 10)`
`calculate_readability`	📖 Readability metrics	`calculate_readability(text)`

Document Management Tools

Tool	Description	Example
`add_document`	📝 Add new document	`add_document("id", "title", "content")`
`get_document`	📄 Retrieve document	`get_document("doc_001")`
`delete_document`	🗑️ Delete document	`delete_document("old_doc")`
`list_documents`	📋 List all documents	`list_documents("Technology")`

Search and Discovery Tools

Tool	Description	Example
`search_documents`	🔍 Semantic search	`search_documents("AI", 5)`
`search_by_tags`	🏷️ Tag-based search	`search_by_tags(["AI", "tech"])`
`get_collection_stats`	📊 Collection statistics	`get_collection_stats()`

📊 Sample Data

The server comes pre-loaded with 16 diverse documents covering:

Category	Documents	Topics
Technology	4	AI, Quantum Computing, Privacy, Blockchain
Science	3	Space Exploration, Healthcare, Ocean Conservation
Environment	2	Climate Change, Sustainable Agriculture
Society	3	Remote Work, Mental Health, Transportation
Business	2	Economics, Digital Privacy
Culture	2	Art History, Wellness

Sample Document Structure

{
  "id": "doc_001",
  "title": "The Future of Artificial Intelligence",
  "content": "Artificial intelligence is rapidly transforming...",
  "author": "Dr. Sarah Chen",
  "category": "Technology",
  "tags": ["AI", "technology", "future", "ethics"],
  "language": "en",
  "created_at": "2024-01-15T10:30:00"
}

🏗️ Project Structure

document-analyzer/
├── 📁 analyzer/                    # Core analysis engine
│   ├── __init__.py
│   └── document_analyzer.py       # Sentiment, keywords, readability
├── 📁 storage/                     # Document storage system
│   ├── __init__.py
│   └── document_storage.py        # JSON storage, search, management
├── 📁 data/                        # Sample data
│   ├── __init__.py
│   └── sample_documents.py        # 16 sample documents
├── 📄 fastmcp_document_analyzer.py # 🌟 Main FastMCP server
├── 📄 test_fastmcp_analyzer.py    # Comprehensive test suite
├── 📄 requirements.txt            # Python dependencies
├── 📄 documents.json              # Persistent document storage
├── 📄 README.md                   # This documentation
├── 📄 FASTMCP_COMPARISON.md       # FastMCP vs Standard MCP
├── 📄 .gitignore                  # Git ignore patterns
└── 📁 venv/                       # Virtual environment (optional)

🔄 API Reference

Document Analysis

`analyze_document(document_id: str) -> Dict[str, Any]`

Performs comprehensive analysis of a document.

Parameters:

document_id (str): Unique document identifier

Returns:

{
  "document_id": "doc_001",
  "title": "Document Title",
  "sentiment_analysis": {
    "overall_sentiment": "positive",
    "confidence": 0.85,
    "vader_scores": {...},
    "textblob_scores": {...}
  },
  "keywords": [
    {"keyword": "artificial", "frequency": 5, "relevance_score": 2.3}
  ],
  "readability": {
    "flesch_reading_ease": 45.2,
    "reading_level": "Difficult",
    "grade_level": "Grade 12"
  },
  "basic_statistics": {
    "word_count": 119,
    "sentence_count": 8,
    "paragraph_count": 1
  }
}

`get_sentiment(text: str) -> Dict[str, Any]`

Analyzes sentiment of any text.

Parameters:

text (str): Text to analyze

Returns:

{
  "overall_sentiment": "positive",
  "confidence": 0.85,
  "vader_scores": {
    "compound": 0.7269,
    "positive": 0.294,
    "negative": 0.0,
    "neutral": 0.706
  },
  "textblob_scores": {
    "polarity": 0.5,
    "subjectivity": 0.6
  }
}

Document Management

`add_document(...) -> Dict[str, str]`

Adds a new document to the collection.

Parameters:

id (str): Unique document ID
title (str): Document title
content (str): Document content
author (str, optional): Author name
category (str, optional): Document category
tags (List[str], optional): Tags list
language (str, optional): Language code

Returns:

{
  "status": "success",
  "message": "Document 'my_doc' added successfully",
  "document_count": 17
}

Search and Discovery

`search_documents(query: str, limit: int = 10) -> List[Dict[str, Any]]`

Performs semantic search across documents.

Parameters:

query (str): Search query
limit (int): Maximum results

Returns:

[
  {
    "id": "doc_001",
    "title": "AI Document",
    "similarity_score": 0.8542,
    "content_preview": "First 200 characters...",
    "tags": ["AI", "technology"]
  }
]

🧪 Testing

Run All Tests

python test_fastmcp_analyzer.py

Test Categories

✅ Server Initialization: FastMCP server setup
✅ Sentiment Analysis: VADER and TextBlob integration
✅ Keyword Extraction: TF-IDF and frequency analysis
✅ Readability Calculation: Multiple readability metrics
✅ Document Analysis: Full document processing
✅ Document Search: Semantic similarity search
✅ Collection Statistics: Analytics and insights
✅ Document Management: CRUD operations
✅ Tag Search: Tag-based filtering

Expected Test Output

=== Testing FastMCP Document Analyzer ===

✓ FastMCP server module imported successfully
✓ Server initialized successfully
✓ Sentiment analysis working
✓ Keyword extraction working
✓ Readability calculation working
✓ Document analysis working
✓ Document search working
✓ Collection statistics working
✓ Document listing working
✓ Document addition and deletion working
✓ Tag search working

=== All FastMCP tests completed successfully! ===

📚 Documentation

Additional Resources

📖 FastMCP Documentation
📖 MCP Protocol Specification
📖 FASTMCP_COMPARISON.md - FastMCP vs Standard MCP

Key Concepts

Sentiment Analysis

Uses dual-engine approach:

VADER: Rule-based, excellent for social media text
TextBlob: Machine learning-based, good for general text

Keyword Extraction

Combines multiple approaches:

TF-IDF: Term frequency-inverse document frequency
Frequency Analysis: Simple word frequency counting
Relevance Scoring: Weighted combination of both methods

Readability Metrics

Provides multiple readability scores:

Flesch Reading Ease: 0-100 scale (higher = easier)
Flesch-Kincaid Grade: US grade level
ARI: Automated Readability Index

Document Search

Uses TF-IDF vectorization with cosine similarity:

Converts documents to numerical vectors
Calculates similarity between query and documents
Returns ranked results with similarity scores

🤝 Contributing

Development Setup

# Clone repository
git clone <repository-url>
cd document-analyzer

# Create development environment
python -m venv venv
source venv/Scripts/activate  # Windows
pip install -r requirements.txt

# Run tests
python test_fastmcp_analyzer.py

Adding New Tools

FastMCP makes it easy to add new tools:

@mcp.tool
def my_new_tool(param: str) -> Dict[str, Any]:
    """
    🔧 Description of what this tool does.

    Args:
        param: Parameter description

    Returns:
        Return value description
    """
    # Implementation here
    return {"result": "success"}

Code Style

Use type hints for all functions
Add comprehensive docstrings
Include error handling
Follow PEP 8 style guidelines
Add emoji icons for better readability

Testing New Features

Add your tool to the main server file
Create test cases in the test file
Run the test suite to ensure everything works
Update documentation as needed

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

FastMCP Team for the excellent framework
NLTK Team for natural language processing tools
TextBlob Team for sentiment analysis capabilities
Scikit-learn Team for machine learning utilities

Made with ❤️ using FastMCP

🚀 Ready to analyze documents? Start with python fastmcp_document_analyzer.py

🔍 FastMCP Document Analyzer

📋 Table of Contents

🌟 Features

📖 Document Analysis

🗂️ Document Management

🚀 FastMCP Advantages

🚀 Quick Start

1. Clone and Setup

2. Install Dependencies

3. Initialize NLTK Data

4. Run the Server

5. Test Everything

📦 Installation

System Requirements

Dependencies

Optional: Virtual Environment

🔧 Usage

Starting the Server

Default (STDIO Transport)

HTTP Transport (for web services)

With Custom Host

Basic Usage Examples

🛠️ Available Tools

Core Analysis Tools

Document Management Tools

Search and Discovery Tools

📊 Sample Data

Sample Document Structure

🏗️ Project Structure

🔄 API Reference

Document Analysis

analyze_document(document_id: str) -> Dict[str, Any]

get_sentiment(text: str) -> Dict[str, Any]

Document Management

add_document(...) -> Dict[str, str]

Search and Discovery

search_documents(query: str, limit: int = 10) -> List[Dict[str, Any]]

🧪 Testing

Run All Tests

Test Categories

Expected Test Output

📚 Documentation

Additional Resources

Key Concepts

Sentiment Analysis

Keyword Extraction

Readability Metrics

Document Search

🤝 Contributing

Development Setup

Adding New Tools

Code Style

Testing New Features

📄 License

🙏 Acknowledgments

Resources

Appeared in Searches

Latest Blog Posts

MCP directory API

`analyze_document(document_id: str) -> Dict[str, Any]`

`get_sentiment(text: str) -> Dict[str, Any]`

`add_document(...) -> Dict[str, str]`

`search_documents(query: str, limit: int = 10) -> List[Dict[str, Any]]`