What is Mcphack

MCPHack is a hackathon repository focused on developing a multi-agent system called FactCheckBot, which is designed to identify, verify, and respond to factual claims on social media platforms like Twitter (X) using the Model Context Protocol (MCP).

Use cases

Use cases for MCPHack include responding to breaking news on social media, verifying claims made by public figures, and providing corrections or clarifications to misinformation circulating online.

How to use

To use MCPHack, developers can clone the repository, set up the necessary environment, and deploy the FactCheckBot system. Users can input tweets or threads, and the system will process them through its agents to provide fact-checking results.

Key features

Key features of MCPHack include modular agent roles for specific tasks (e.g., claim extraction, evidence retrieval, verification, and explanation), dynamic coordination through shared memory, and the ability to generate concise and explainable verdicts.

Where to use

MCPHack can be used in social media monitoring, misinformation detection, and fact-checking services, particularly in environments where timely and accurate information is critical.

InfoSys Cambridge Hackathon: Multi-Agent Fact-Checking for Social Media

FactCheckBot is a multi-agent system designed to identify, verify, and respond to factual claims on social media platforms like Twitter (X). Built using the Model Context Protocol (MCP), the system decomposes the complex task of fact-checking into modular, intelligent agents — each responsible for a specific decision or reasoning step.

What It Does

Given a tweet or thread, FactCheckBot:

1. Extracts factual claims
2. Searches reliable external sources
3. Evaluates the claim based on supporting or contradicting evidence
4. Falls back on crowdsourced consensus for recent events
5. Generates a concise, explainable verdict
6. Posts a correction or clarification as a reply

This system is designed to combat misinformation with speed, context, and transparency.

Agent Roles

All agents operate on a shared memory structure defined by the Model Context Protocol, allowing dynamic coordination and context-aware reasoning.

Example Workflow

Input Tweet:

“Massive explosion reported in Ankara right now.”

Agent Flow:

1. ClaimExtractorAgent detects a location-based factual claim.
2. RetrieverAgent finds no results in verified sources — too recent.
3. CrowdSignalAgent clusters 10+ independent geolocated users in Ankara posting similar claims.
4. VerifierAgent assigns a tentative credibility score with fallback reasoning.
5. ExplainerAgent formats the reply:

   “🚨 Developing: This event is not yet reported by major outlets, but multiple users in Ankara are sharing photos and consistent details. Treat as likely true, pending confirmation. [Context]”

6. PosterAgent publishes the reply.

Real-Time Consensus via CrowdSignalAgent

In cases where a claim cannot be confirmed through traditional sources (e.g., breaking news or remote events), the system uses CrowdSignalAgent to:

• Search for tweets matching the claim
• Filter for geolocated, domain-relevant, or independent sources
• Evaluate consensus within a time window
• Provide a fallback credibility score if traditional verification is unavailable

This helps prevent false negatives and enhances trustworthiness in time-sensitive cases.