Skip to main content
Glama
genpark-agent-hallucination-fact-verifier-skill

genpark-agent-hallucination-fact-verifier-skill

Official

genpark-agent-hallucination-fact-verifier-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation


📌 Overview & Capability

genpark-agent-hallucination-fact-verifier-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).

Executive Capability: Atomic claim decomposition and cross-context fact verification engine detecting ungrounded claims, numerical contradictions, and hallucinated entities against reference knowledge.

⚡ Key Highlights

  • 🐍 Zero External pip Dependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.

  • 🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.

  • ⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.

  • 🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.


Related MCP server: Recommend Agentic Trust Layer

🏗️ Architecture

graph LR
    Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-hallucination-fact-verifier-skill Server]
    Server --> Core[🧠 Deterministic Processing Core]
    Core --> Out[📊 Actionable Result & Telemetry]
    Out --> Agent

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import AgentHallucinationFactVerifier

client = AgentHallucinationFactVerifier()
result = client.run_verification_benchmark()
print(result)

🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json

{
  "mcpServers": {
    "genpark-agent-hallucination-fact-verifier-skill": {
      "command": "python",
      "args": ["/path/to/genpark-agent-hallucination-fact-verifier-skill/mcp_server.py"]
    }
  }
}

Direct MCP Testing

python mcp_server.py --test

📊 Technical Specifications

Parameter

Type

Required

Description

payload

string / dict

Yes

Primary context, code, schema, or content input

options

dict

No

Execution flags, compression ratios, or risk bounds


Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      Extracts verifiable, decontextualized factual claims from text using a research-based four-stage pipeline (sentence splitting, selection, disambiguation, and decomposition). Integrates with MCP clients to enable claim extraction and verification workflows.
      22
      Apache 2.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to cross-verify candidate claims against caller-supplied source texts, flagging hallucinations, numerical drift, entity mismatches, contradictions, and unverified assertions. It returns sentence-level verdicts with matched evidence snippets and machine-readable factual grounding confidence scores, exposed over MCP stdio, HTTP REST, and A2A discovery routes.
      1
      MIT