Create a statistical ensemble of Random Trees to model population-level recall from narrative text, configurable by ensemble size, branching factor, and recall depth.
45,057 tools. Last updated 2025-12-18 04:01
- Analyze narrative text at customizable abstraction levels by traversing its structure, extracting summaries based on depth, branching factor, and recall settings for enhanced understanding.
- Determine the optimal traversal depth for narrative analysis to reach a target recall length, using configurable branching and depth parameters for efficient information retrieval.
- Convert narrative text into a structured tree model with configurable branching and depth parameters for efficient encoding and recall of information in the Narrative Graph MCP.
- Generate optimized CRUD operation prompts for backend development, tailored to Java/Kotlin package paths, to enhance LLM content creation for RT-Prompt-MCP.
- Retrieve specific RUM metrics from your Datadog organization for monitoring web application performance and user experience tracking.Apache 2.0
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