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genpark-contextual-memory-pruning-importance-attributor-skill MCP Server

Alpha-Park/genpark-contextual-memory-pruning-importance-attributor-skill

Score: 58.2 Rank #7070 MCP Server
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Autonomous Long-Horizon Agentic Memory Pruning & Importance Attributor. Evaluates multi-turn episodic memory buffers, calculates multi-factor salience scores (recency decay, user priority pinning, emotional valence, and entity graph connectivity), and deterministically prunes low-utility noise to respect model context limits.

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Overview

Alpha-Park/genpark-contextual-memory-pruning-importance-attributor-skill is a Python tool licensed under MIT. Autonomous Long-Horizon Agentic Memory Pruning & Importance Attributor. Evaluates multi-turn episodic memory buffers, calculates multi-factor salience scores (recency decay, user priority pinning, emotional valence, and entity graph connectivity), and deterministically prunes low-utility noise to respect model context limits.

Ranked #7070 out of 24846 indexed tools.

Ecosystem

Python MIT

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 7

7 stars → early stage

Freshness 25% 12d ago

Last commit 12d ago → recently updated

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 0

0 contributors → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 0
Description Detailed
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Contributors medium impact

Single-contributor projects carry bus-factor risk — welcoming contributors boosts confidence

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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AgentRank score for Alpha-Park/genpark-contextual-memory-pruning-importance-attributor-skill
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Matched Queries

topic:mcptopic:mcp-servertopic:model-context-protocol

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