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URL: https://glama.ai/mcp/servers/integrations/mlflow

⇱ MLflow | Glama


  • Why this server?

    Provides tools for querying, debugging, and evaluating MLflow traces generated during research and analysis workflows.

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    45-tool MCP server for video analysis, deep research, content extraction, web search, and Weaviate knowledge storage. Powered by Gemini 3.1 Pro.
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    MIT
  • Why this server?

    Provides tools for managing MLflow experiments, runs, models, registered models, model versions, and webhooks.

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    Exposes Databricks REST APIs as MCP tools for managing and querying a Databricks workspace, including clusters, jobs, SQL, Unity Catalog, and more.
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    MIT
  • Why this server?

    Provides comprehensive tools for managing MLflow experiments, runs, registered models, model versions, logged models (MLflow 3), traces, assessments, webhooks, and prompt-optimization jobs. Includes aggregation tools and MCP resources for efficient AI-driven workflows.

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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.
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  • Why this server?

    Discovers and scans MLflow platforms to identify security risks and verify the provenance of AI models.

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    AI supply chain security scanner for MCP servers and AI agents. 18 tools for CVE scanning, blast radius mapping, CIS benchmarks, SBOM generation, and compliance enforcement across OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and EU AI Act.
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    Apache 2.0
  • Why this server?

    Supports integration with MLflow model registries to track and generate human-readable explanations for registered machine learning models.

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    Provides deterministic, plain-English narratives explaining machine learning model predictions via the Model Context Protocol. It enables users to query classification factors directly through natural language, bypassing the need for complex plots or manual code execution.
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  • Why this server?

    Provides tools for interacting with MLflow experiments, runs, and registered models, enabling browsing of experiments, retrieving run details with metrics and parameters, and querying the model registry with filtering and pagination support.

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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
  • Why this server?

    Provides tools for experiment tracking, model registry, and artifact operations using MLflow.

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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.
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  • Why this server?

    Provides searchable documentation for MLflow experiment tracking as part of the MLOps knowledge base.

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    Provides AI assistants with searchable access to documentation from 170+ curated repositories and 1000+ popular GitHub projects across 20+ categories including trading, AI/ML, DevOps, and web development.
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  • Why this server?

    Enables logging of repository snapshots (git history, filesystem stats, environment markers) as artifacts to MLflow experiments for tracking and versioning.

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    Enables searching text across configured local workspaces using ripgrep. Provides secure text search capabilities within defined workspace boundaries through both MCP server and CLI interfaces.
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