What is Analytical Mcp

The analytical-mcp is a specialized Model Context Protocol (MCP) server designed to enhance AI capabilities through advanced analytical, research, and natural language processing tools.

Use cases

Use cases for analytical-mcp include analyzing datasets for insights, verifying research claims, extracting entities from text, and performing sentiment analysis on customer feedback.

How to use

To use analytical-mcp, clone the repository, install dependencies using npm, set up your environment variables with the required API keys, and build the project. You can then invoke various analytical tools and NLP features through specific parameters.

Key features

Key features include dataset analysis, decision analysis, correlation analysis, regression analysis, time series analysis, hypothesis testing, and advanced NLP capabilities such as named entity recognition, sentiment analysis, and relationship extraction.

Where to use

analytical-mcp can be used in various fields including data analysis, research verification, natural language processing applications, and any domain requiring structured problem-solving tools.

Analytical MCP Server

A specialized Model Context Protocol (MCP) server providing advanced analytical, research, and natural language processing capabilities.

Key Features

Analytical Tools

• Dataset Analysis
• Decision Analysis
• Correlation Analysis
• Regression Analysis
• Time Series Analysis
• Hypothesis Testing

Advanced NLP Capabilities

• Enhanced Fact Extraction
• Named Entity Recognition
• Coreference Resolution
• Relationship Extraction
• Sentiment Analysis
• Text Similarity
• Part of Speech Tagging
• Lemmatization
• Spell Checking

Installation

Prerequisites

• Node.js (v20+)
• npm
• Exa API key (for research and advanced NLP capabilities)

Setup

1. Clone the repository
2. Install dependencies:

   npm install
   

3. Set up your environment variables:

   # Copy the example environment file
   cp .env.example .env
   
   # Edit .env and add your API keys
   # You'll need an Exa API key for research functionality
   

4. Build the project:

   npm run build
   

Usage

Running Tools

Each tool can be invoked with specific parameters. Example:

// Analyze a dataset
const datasetAnalysis = await analyzeDataset([1, 2, 3, 4, 5], 'summary');

// Verify research claims
const researchVerification = await researchVerification.verifyResearch({
  query: 'Climate change impacts',
  sources: 3
});

// Extract entities from text
const entities = await advancedNER.recognizeEntities(
  "Apple Inc. is planning to open a new headquarters in Austin, Texas."
);

Advanced NLP Demo

You can run the included NLP demo to see the advanced capabilities in action:

npm run build
node examples/advanced_nlp_demo.js

Development

Available Scripts

• npm run build: Compile TypeScript
• npm test: Run all tests
• npm run test:integration: Run integration tests only
• npm run test:exa: Run Exa Research API tests
• npm run test:research: Run Research Verification tests
• npm run test:server: Run Server Tool Registration tests
• npm run lint: Check code quality
• npm run format: Format code
• npm run nlp:demo: Run advanced NLP demo

Test Scripts

We provide dedicated scripts for running specific test suites:

Unix/Linux/Mac

# Run all integration tests with a summary report
./tools/run-all-integration-tests.sh

# Run specific test suites
./tools/run-exa-tests.sh
./tools/run-research-tests.sh
./tools/run-server-tests.sh
./tools/run-api-key-tests.sh
./tools/run-data-pipeline-tests.sh
./tools/run-market-analysis-tests.sh

Windows

# Run all integration tests with a summary report
.\tools\run-all-integration-tests.bat

Key Technologies

• TypeScript
• Model Context Protocol SDK
• Exa API for Research and NLP
• Natural Language Processing libraries
• Jest for Testing

Advanced NLP Implementation

The Analytical MCP Server implements advanced NLP features using:

• Exa research API for context-aware entity recognition
• Natural language toolkit for basic NLP operations
• Custom rule-based fallback mechanisms for offline capabilities
• Enhanced fact extraction with confidence scoring
• Relationship extraction between entities

For detailed information, see the Advanced NLP documentation.

Required API Keys

This project requires the following API key:

• EXA_API_KEY: Used for research integration and advanced NLP

The .env.example file contains all available configuration options:

• API keys
• Feature flags
• Cache settings
• NLP configuration
• Server configuration

Copy this file to .env in your project root and update with your actual API keys to get started.

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License