MCP Research Tool

MCP Research Tool

Stable Release
TypeScript AI Cloudflare Workers

Overview

A powerful, AI-driven research orchestration service that gathers, analyzes, and synthesizes information from multiple sources to provide comprehensive answers to complex queries. Built on Cloudflare Workers, this service leverages multiple specialized tools and AI capabilities to deliver well-structured, properly cited research results.

This service transforms natural language queries into structured research tasks, intelligently selecting and orchestrating various specialized tools to gather relevant information. It then synthesizes the collected data into a coherent, well-cited response with confidence indicators.

Special thanks to the Octotools team for inspiring the orchestration component architecture, which uses tool cards to determine the optimal set of tools for each research task.

Key Features

Intelligent Query Analysis

  • Advanced analysis of user queries to understand intent, entities, and constraints
  • Query optimization for each specialized research tool
  • Automatic URL and context extraction
  • Dynamic tool selection based on relevance scoring

Multi-tool Orchestration

  • Dynamic tool selection based on query context
  • Parallel execution with smart retry logic
  • Automatic query adaptation for each tool
  • Intelligent tool reuse across research iterations

Iterative Research Process

  • Multiple research iterations with targeted gap analysis
  • Highly focused follow-up queries for missing information
  • Effective handling of follow-up iterations
  • Smart termination when sufficient information is gathered

AI-powered Synthesis

  • Combines information from diverse sources into comprehensive answers
  • Proper citation and source attribution
  • Structured formatting with sections and highlights

Quality Assurance

  • Confidence scoring for individual results and overall synthesis
  • Source credibility assessment
  • Relevance filtering of results
  • Batch processing for efficient analysis

Performance Optimization

  • Built-in caching system using Cloudflare KV
  • Parallel execution of tools
  • Smart retry logic for API failures
  • Efficient batching for result assessment

Architecture

Core Components

Orchestrator (src/orchestrator.ts)

  • Coordinates the entire research process
  • Manages tool selection and execution with intelligent reuse
  • Handles result aggregation and synthesis
  • Supports metadata-enriched tool execution for better context

Query Optimizer (src/queryOptimizer.ts)

  • Analyzes queries for intent and context
  • Optimizes queries for different tools
  • Extracts entities, URLs, and YouTube video IDs

Tool Manager

Handles tool selection based on query relevance, manages execution with retry logic, and implements caching for better performance. It provides unified error handling and standardized response formatting.

Result Processor (src/resultProcessor.ts)

  • Assesses result relevance with batch processing
  • Analyzes information gaps and creates targeted follow-up queries
  • Synthesizes final results
  • Implements parallel processing for better performance

Formatter (src/formatter.ts)

  • Structures output with proper formatting
  • Handles citation management
  • Provides confidence indicators

Research Process

The research process follows these steps:

  1. Query Analysis: The query is analyzed to understand intent, extract entities, identify URLs, and determine query types.
  2. Tool Selection:
    • Tools are selected based on relevance to the query
    • On initial iterations, previously used tools are filtered out
    • For follow-up queries, tools can be reused to explore new aspects
  3. Query Optimization: The query is optimized for each selected tool to maximize relevance.
  4. Tool Execution:
    • Tools are executed in parallel with metadata context
    • Automatic extraction of URLs and YouTube video IDs when relevant
    • Results are cached for performance
  5. Relevance Assessment:
    • Results are assessed for relevance against the original query
    • Processing occurs in batches to handle large result sets efficiently
    • Diversity is ensured through batch processing
  6. Gap Analysis:
    • Information gaps are identified
    • Targeted follow-up queries focus on missing aspects
    • Analysis provides specific missing aspects and explains the gaps
  7. Iteration:
    • Process repeats with follow-up queries until gaps are filled
    • Tools can be reused between iterations for different aspects
    • Each iteration builds on previous knowledge
  8. Synthesis:
    • All relevant results are synthesized into a comprehensive answer
    • Sources are properly cited and organized
    • Confidence score is calculated based on source quality and content

Technical Implementation

The MCP Research Tool is implemented using a functional programming approach with TypeScript. The core components work together to provide a comprehensive research experience through intelligent tool orchestration and result processing.

Query Optimizer

The query optimizer analyzes natural language queries to extract key information like entities, URLs, and YouTube video IDs. It optimizes queries for each research tool to maximize relevance.

Tool Manager

Handles tool selection based on query relevance, manages execution with retry logic, and implements caching for better performance. It provides unified error handling and standardized response formatting.

Result Processor

Processes and filters results based on relevance, analyzing information gaps and creating targeted follow-up queries. It supports batch processing for efficient handling of large result sets.

Orchestrator

Coordinates the entire research process, managing tool selection, execution, result aggregation, and synthesis. Implements intelligent caching strategies and supports multi-stage research iteration.

Cache Manager

Provides multi-level caching with both memory and Cloudflare KV stores for optimal performance. Implements TTL-based expiration and handles cache invalidation logic.

Research Tools

Integrated Tools

  • Web Search
    • Brave Search: Privacy-focused web search with broad coverage
    • Tavily Search: AI-powered search with enhanced relevance
  • Technical Information
    • GitHub Repository Search: Find relevant repositories
    • GitHub Code Search: Search for code examples and implementations
    • Stack Exchange: Technical Q&A from Stack Overflow and related sites
  • Academic Research
    • arXiv: Academic papers and preprints
    • Patent Search: Intellectual property and innovation tracking
  • Current Events
    • News API: Recent news and developments
    • Media Monitoring: Current events tracking
  • Content Extraction
    • Fire Crawl: Web content extraction and analysis
    • YouTube Transcript: Video content transcription
  • Reference Information
    • Wikipedia Search: General knowledge and reference
    • Book Search: Literature and bibliographic information

Usage Example

Basic Usage

The MCP Research Tool can be used to conduct comprehensive research on a wide range of topics. Simply provide a natural language query and optional research parameters to get detailed, well-cited results.

Response Structure

Research results include:

  • Answer: A comprehensive synthesized research answer
  • Sources: List of sources with URLs, titles, and relevance
  • Confidence: Overall confidence score (0-1)
  • Metadata: Execution time, iteration count, tools used, and more

Example Research Request

{
  "depth": 3,
  "query": "recent cybersecurity developments and trends 2024-2025"
}

Example Research Results

🟢 High confidence

Recent Developments and Trends in Cybersecurity (2024-2025)

Introduction

The cybersecurity landscape is continuously evolving, with new challenges and solutions emerging regularly. This response synthesizes recent developments and trends in cybersecurity for the years 2024-2025, focusing on technological advancements, strategic frameworks, and emerging threats.

Technological Advancements in Cybersecurity
Decentralized Data Centers
  • The global edge data center market is expected to grow significantly, driven by the rise of 5G networks, the Internet of Things (IoT), AI-driven automation, and the need for low-latency, decentralized data solutions [1].
AI and Machine Learning
  • AI and machine learning are increasingly integrated into cybersecurity solutions, offering enhanced capabilities for real-time threat detection and response [2].
  • Specific advancements include AI-driven threat detection systems for smart vehicles and the use of large language models (LLMs) in cybersecurity applications [3,4].
Strategic Frameworks and Collaboration
Cybersecurity Frameworks
  • New frameworks such as the Cybersecurity Resilience and Law Enforcement Collaboration (CyRLEC) Framework emphasize proactive prevention, early detection, and collaboration with law enforcement to combat cyber threats [5].
  • Integration of established frameworks like NIST and ISO/IEC 27001 continues to play a critical role in shaping cybersecurity strategies across various industries [2].
Collaborative Efforts
  • There is a growing focus on collaborative approaches, particularly between the private sector and government agencies, to enhance cybersecurity measures and infrastructure [5].
Emerging Threats and Challenges
Quantum Computing
  • The development of quantum computing poses new challenges for cybersecurity, with potential threats to encryption methods and data security [6].
  • Concurrently, quantum technology also presents opportunities for enhancing cybersecurity solutions [6].
Smart Device Security
  • With the proliferation of connected devices, securing IoT and smart devices remains a critical challenge. Efforts are concentrated on developing robust security protocols and systems to protect against vulnerabilities [4].
Conclusion

The years 2024-2025 mark significant progress in the field of cybersecurity, with advancements in technology and strategic frameworks playing pivotal roles. However, the landscape continues to evolve with new threats emerging, necessitating ongoing research, development, and collaboration to safeguard digital assets and infrastructures.

Citations:
  1. GlobeNewswire. (2025). From Cloud to Edge: Why Decentralized Data Centers Are the Future of IT & Telecom. Link
  2. GlobeNewswire. (2025). Integrating Cybersecurity Frameworks into IT Security: A Comprehensive Analysis of Threat Mitigation Strategies and Adaptive Technologies. Link
  3. GlobeNewswire. (2025). Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection. Link
  4. GlobeNewswire. (2025). AI Emergency Preparedness: Examining the federal government's ability to detect and respond to AI-related national security threats. Link
  5. GlobeNewswire. (2025). Building a Resilient Cybersecurity Posture: A Framework for Leveraging Prevent, Detect and Respond Functions and Law Enforcement Collaboration. Link
  6. GlobeNewswire. (2025). A Review of Quantum Cybersecurity: Threats, Risks and Opportunities. Link
Metadata
  • Time: 79838ms
  • Tools: tavily_search, news_api_search, github_repo_search, stack_exchange_search, arxiv_search
  • Query Types: current_events, technical
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