Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-semantic-entropy-hallucination-estimator-skill Check this LLM response for hallucination using semantic entropy."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A zero-dependency Python standard library skill for estimating LLM hallucination and confabulation via semantic entropy (Kuhn et al.). Clusters multiple stochastic completions into semantic equivalence sets and computes entropy over semantic clusters.
graph TD
A[Stochastic LLM Samples] --> B[N-gram Semantic Equivalence Grouping]
B --> C[Cluster Assignment]
C --> D[Compute Cluster Probability P_C]
D --> E[Semantic Entropy -sum P_C log P_C]
E --> F{Entropy >= Threshold?}
F -->|Yes| G[Flag Hallucination / Confabulation]
F -->|No| H[Verified Factual Alignment]Related MCP server: groundlens-deprecated
Semantic Equivalence Clustering: Group responses by semantic meaning rather than exact token matches.
Pure Python 3.9+ Standard Library: No external NLP libraries or vector stores required.
Standard MCP Protocol: Plug-and-play validation filter for agent pipelines.
Platform: GenPark AI
MCP Registry: GenPark MCP Hub
This server cannot be deployed
Explainable ambiguity signals for text. Submitted text is processed but never retained.
Sentiment, toxicity, entity extraction, PII, translation, summary, QA, fraud scoring, safety audit.
Extract structured data points from research papers and other documents with an LLM.
Multi-LLM entity enrichment: schemas, single/batch enrichment, fusion, model benchmarks.
