Scientific Fraud Detection
@apifyforge
About Scientific Fraud Detection
Scientific fraud detection as a live MCP server — screen any research topic, author, or paper for statistical fabrication, p-hacking, publication bias, citation manipulation, data duplication, causal contamination, and HARKing using 8 forensic tools backed by 16 real-time academi
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"scientific-fraud-detection-mcp": {
"url": "https://ryanclinton--scientific-fraud-detection-mcp.apify.actor/mcp"
}
}
}Tools
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Overview
What is Scientific Fraud Detection?
Scientific Fraud Detection is a live MCP server that screens any research topic, author, or paper for statistical fabrication, p-hacking, publication bias, citation manipulation, data duplication, and causal contamination. It provides 8 forensic tools backed by 16 real-time academic sources (OpenAlex, PubMed, Semantic Scholar, etc.) and is built for research integrity officers, meta-analysts, and AI coding assistants.
How to use Scientific Fraud Detection?
No API keys or environment setup are required. Add the server URL https://scientific-fraud-detection-mcp.apify.actor/mcp to your MCP client’s configuration file (Claude Desktop, Cursor, or Windsurf). Once connected, prompt your AI assistant to run any of the 8 tools with a single query string; results stream back as structured JSON.
Key features of Scientific Fraud Detection
- GRIM test for detecting impossible reported means given sample size
- SPRITE reconstruction of feasible integer distributions
- Benford’s law first-digit analysis for fabricated data detection
- P-curve right-skew test and z-curve EM algorithm for p-hacking
- Vevea-Hedges selection model for publication-bias correction
- TERGM citation anomaly detection and MinHash near-duplicate detection
Use cases of Scientific Fraud Detection
- Research integrity assessment: integrity officers screen papers for statistical and citation anomalies in one session.
- Replication crisis meta-analysis: feed an entire subfield into p-curve/z-curve tools to estimate replication rates.
- AI assistant research grounding: verify scientific claims in real time before responding.
- Citation manipulation investigation: detect citation rings, excessive self-citation, and coerced citation patterns.
FAQ from Scientific Fraud Detection
Do I need API keys to use the server?
No. The server requires no API keys or environment setup — just add the URL to your MCP client configuration.
What data sources are queried?
The server queries up to 16 academic sources in parallel, including OpenAlex, PubMed, Semantic Scholar, arXiv, Crossref, CORE, Europe PMC, ORCID, DBLP, and others.
How many tools are available?
Eight tools are exposed: audit_statistical_consistency, analyze_p_curve_z_curve, fit_selection_model_meta_analysis, detect_citation_network_anomalies, detect_image_and_text_forensics, trace_causal_contamination, self_calibrate_detection, and screen_publication_bias_and_harking.
Can I use this with any MCP client?
Yes. The server works natively with Claude Desktop, Cursor, Windsurf, and any MCP-compatible AI assistant.
What output format does the server return?
All results are returned as structured JSON, ready for downstream analysis pipelines or further processing.
Frequently asked questions
Do I need API keys to use the server?
No. The server requires no API keys or environment setup — just add the URL to your MCP client configuration.
What data sources are queried?
The server queries up to 16 academic sources in parallel, including OpenAlex, PubMed, Semantic Scholar, arXiv, Crossref, CORE, Europe PMC, ORCID, DBLP, and others.
How many tools are available?
Eight tools are exposed: `audit_statistical_consistency`, `analyze_p_curve_z_curve`, `fit_selection_model_meta_analysis`, `detect_citation_network_anomalies`, `detect_image_and_text_forensics`, `trace_causal_contamination`, `self_calibrate_detection`, and `screen_publication_bias_and_harking`.
Can I use this with any MCP client?
Yes. The server works natively with Claude Desktop, Cursor, Windsurf, and any MCP-compatible AI assistant.
What output format does the server return?
All results are returned as structured JSON, ready for downstream analysis pipelines or further processing.
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