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622,070 tools. Updated 2026-09-29 17:50

"Resources for learning about data analysis" matching MCP tools:

  • Retrieve detailed specifications of WindAI's machine learning model including architecture, training data, and accuracy metrics to understand prediction methodology.
    MIT
  • Analyzes a repository to generate a Knowledge Extraction Report with architectural insights, design decisions, data flow, strengths, risks, and learning paths.
    MIT

Matching MCP Servers

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    license
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    quality
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    maintenance
    An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.
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Matching MCP Connectors

  • Query structured training programs (learning paths) to retrieve metadata, enrollments, and student lists, with pagination and filters.
    MIT
  • Generate histograms to visualize data distribution and frequency patterns for analysis in machine learning research.
    MIT
  • Store root cause analysis, research sources, and fix details for an error incident to build an audit trail for learning from past fixes after investigating and deploying a solution.
    MIT
  • Discover MCP resources for sports data providers, including capability maps, dispatcher catalogues, and reference data, to enable cross-provider odds and stats comparison.
    MIT
  • Search Jina AI's official blog for articles about AI, machine learning, neural search, embeddings, and Jina products to find documentation, tutorials, announcements, and technical deep-dives.
    Apache 2.0
  • Retrieve lecture notes, explanations, and learning objectives for any module. Use this to answer conceptual questions about course topics.
    Apache 2.0
  • Retrieve detailed information about a Panther data model, including Python body code and UDM mappings for security monitoring analysis.
    Python
    Apache 2.0
  • Store structured memories about entities in six layers (goal, context, emotion, implementation, caveat, learning) to preserve non-obvious goals, failures, and decisions.
    MIT
  • Analyze downstream dependencies affected by changes to any dbt resource, with auto-detection and actionable recommendations for running impacted models, tests, and other resources.
    MIT
  • Export scanned or loaded AWS resource data from memory to a local directory, enabling offline analysis and later reloading for review.
    AGPL 3.0