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    MLflow MCP — 82 tools for experiments, runs, registered models, traces, assessments. MLflow 3 GenAI traces support with analyze-failed-traces workflow Prompt.
    82
    30 npm
    1
    MIT
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    A Model Context Protocol server that enables LLMs to interact with MLflow tracking servers, allowing users to query experiments, analyze runs, compare metrics, manage the model registry, and promote models through natural language.
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    76 PyPI
    15
    MIT
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    Enables AI assistants to interact with MLflow experiments, runs, and registered models. Supports browsing experiments, retrieving run details with metrics and parameters, and querying the model registry through natural language.
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    MIT
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    A natural language interface for MLflow that allows users to query and manage their machine learning experiments and models using plain English through the Model Context Protocol.
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    Enables Claude to interact with ML experiment tracking, model registries, and deployment pipelines across popular MLOps platforms like MLflow.
    MIT
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    Enables creation, management, and deployment of n8n workflows with enhanced context and automation capabilities. Features code extraction, automatic documentation, template generation, and intelligent project organization for streamlined workflow development.
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    Enables AI assistants to perform MLOps workflows such as experiment tracking, model registry, dataset management, pipeline orchestration, and data lineage by wrapping DVC, MLflow, and Git.
    100
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    MIT
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    Enables financial agent workflows to perform agentic retrieval over SEC filings, rank filing types and passages, and synthesize answers from evidence using MCP tools, with MLflow tracing and FinAgentBench evaluation.
    MIT
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    AI-powered fraud detection and investigation platform that exposes tools for querying, scoring, and investigating financial applications using LangGraph, MLflow, and SQLite in-memory.
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    Enables MCP-compatible agents to perform stock analysis and ML workflows, including live market data retrieval, model training, MLflow experiment tracking, champion/challenger model promotion, and Docker deployment.
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