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-personal-agent-information-diet-entropy-pruner-skill audit my reading stream entropy and recommend low-signal sources to prune"
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.
GenPark Personal Agent Information Diet Entropy Pruner applies Shannon information theory, Jaccard lexical novelty metrics, and decibel SNR modeling to optimize personal reading diets.
flowchart TD
A[Daily Ingested Reading & Media Stream] --> B[Category Distribution Histogram]
B --> C[Shannon Information Entropy H]
D[Pairwise Article Token Sets] --> E[Jaccard Redundancy & Echo Chamber Auditor]
F[Source Insight vs Noise Densities] --> G[Decibel SNR Ranking Solver]
C & E & G --> H[Pruning Recommendations & High-Signal Curation]Shannon Entropy Scoring: Quantifies intellectual breadth and flags doomscroll concentration.
Echo Chamber Jaccard Metric: Detects repackaged duplicate ideas across different media outlets.
Decibel SNR Filtering: Surfaces high-density thought leadership while pruning low-signal noise.
Related MCP server: pangram-editorial
from client import PersonalAgentInformationDietEntropyPruner
pruner = PersonalAgentInformationDietEntropyPruner()
res = pruner.rank_content_sources_snr([{"name": "Research", "insight_density": 0.9, "noise_density": 0.1}])
print("SNR:", res["sources_ranked"][0]["snr_db"], "dB")python -m genpark-personal-agent-information-diet-entropy-pruner-skill.mcp_serverMIT
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AI-powered reading triage MCP. 26 tools with a 24h burn timer. Read less, absorb more.
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