Analytical MCP Server: AI-Driven Decisions, Enterprise Efficiency

Key Features of Analytical MCP Server: AI-Driven Decisions, Enterprise Efficiency?

• Data-Driven Intelligence: Processes structured/unstructured data through 12+ analytical models (e.g., regression, NLP parsing)
• Real-Time Validation: Cross-checks hypotheses against live datasets from financial APIs and web sources
• Custom Pipelines: Build modular workflows using drag-and-drop interface or YAML scripting
• Compliance Tools: Automatically redacts PII and flags irregularities in financial reports

Use Cases of Analytical MCP Server: AI-Driven Decisions, Enterprise Efficiency?

Common applications include:

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