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⇱ Reliable sources for use in training language models | Glama


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Reliable sources for use in training language models

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  • Why this server?

    Offers access to file systems, databases, GitHub repositories, web resources, and system tools, providing a broad range of reliable sources.

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    A comprehensive Model Context Protocol server implementation that enables AI assistants to interact with file systems, databases, GitHub repositories, web resources, and system tools while maintaining security and control.
    Last updated
    81
    2
    MIT
  • Why this server?

    Provides functionality to fetch and transform web content in various formats (HTML, JSON, plain text, and Markdown) through simple API calls, enabling retrieval of reliable information from the web.

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    Provides functionality to fetch and transform web content in various formats (HTML, JSON, plain text, and Markdown) through simple API calls.
    Last updated
    101,793
    1
    MIT
  • Why this server?

    Enables interaction with Wikidata API, allowing searching identifiers, extracting metadata, and executing SPARQL queries for structured knowledge.

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    A server implementation for interacting with Wikidata API using the Model Context Protocol, providing tools for searching identifiers, extracting metadata, and executing SPARQL queries.
    Last updated
    5
    46
    MIT
  • Why this server?

    Provides read-only access to MySQL databases, enabling LLMs to inspect database schemas and execute read-only queries for structured data.

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    A Model Context Protocol server providing read-only access to MySQL databases, enabling LLMs to inspect database schemas and execute read-only queries.
    Last updated
    1,058
    1
    MIT
  • Why this server?

    Provides comprehensive security analysis tools for querying the VirusTotal API, enabling detailed security reports on URLs, files, IP addresses, and domains.

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    Provides comprehensive security analysis tools for querying the VirusTotal API, enabling detailed security reports on URLs, files, IP addresses, and domains with automatic relationship data fetching.
    Last updated
    3
    Apache 2.0
  • Why this server?

    Provides AgentQL's data extraction capabilities enabling AI agents to get structured data from unstructured web pages, creating a structured knowledge source.

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    A server that provides AgentQL's data extraction capabilities enabling AI agents to get structured data from unstructured web
    Last updated
    1
    184
    174
    MIT
  • Why this server?

    Enables interaction with GitHub repositories through the GitHub API, allowing file operations, repository management, issue tracking, and code search for code-related information.

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    Enables interaction with GitHub repositories through the GitHub API, allowing file operations, repository management, issue tracking, and code search through natural language commands.
    Last updated
    33
    32
    4
    MIT
  • Why this server?

    Enables AI models to access GitHub repository contents as context, fetching entire repositories, specific file contents, and repository structures for use in AI interactions.

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    Enables AI models to access GitHub repository contents as context, with features to fetch entire repositories, specific file contents, and repository structures for use in AI interactions.
    Last updated
    3
    2,079
    2
    MIT
  • Why this server?

    Enables AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering for knowledge creation.

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    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
    Last updated
    13
    566
    Apache 2.0