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A search for guests for a podcast using Google and LinkedIn

  • Why this server?

    Enables AI models to perform Google searches directly, which addresses the 'search google' part of the request.

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    A Model Context Protocol server implementation that enables AI assistants like Claude to perform Google searches and retrieve web data directly through natural language requests.
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    75
    3
    TypeScript
    MIT License
  • Why this server?

    Offers autonomous web research capabilities, which aligns with the 'search google' task by exploring and validating sources.

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    Enhances LLM applications with deep autonomous web research capabilities, delivering higher quality information than standard search tools by exploring and validating numerous trusted sources.
    65
    Python
    MIT License
    • Apple
  • Why this server?

    Provides AI with access to Bing Search API, thus enabling search functionality.

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    A Model Context Protocol server that integrates with Microsoft Bing Search API, allowing AI assistants to perform web, news, and image searches.
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    19
    Python
    MIT License
    • Apple
  • Why this server?

    Allows AI to interact with LinkedIn data, such as profiles and jobs, fulfilling the 'search linkedin' requirement and potentially helping find podcast guests.

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    Enables AI assistants to interact with LinkedIn data through the Model Context Protocol, allowing profile searches, job discovery, messaging, and network analytics.
    1
    TypeScript
    MIT License
    • Apple
  • Why this server?

    Can generate LinkedIn post drafts, helpful for outreach to potential podcast guests.

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    A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.
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    2
    JavaScript
    MIT License
    • Apple
  • Why this server?

    Integrates Tavily's search API, enabling AI to perform web searches and retrieve information for finding potential podcast guests and information to prepare for the podcast.

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    Integrates Tavily's search API with LLMs to provide advanced web search capabilities, including intelligent result summaries, domain filtering for quality control, and configurable search parameters.
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    64
    9
    JavaScript
    MIT License
    • Linux
  • Why this server?

    Although it is not directly related to searching, it could help organize and manage potential guests and their information.

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    MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution
    4
    Python
    MIT License
    • Apple
  • Why this server?

    If the user's podcast has a website, this could help analyze website traffic to find information about the audience which could be useful in targeting guests.

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    Provides an interface to access Google Analytics Data API through Model Context Protocol (MCP), allowing users to retrieve reports and realtime data from Google Analytics 4 properties.
    JavaScript
  • Why this server?

    Facilitates sophisticated web searches, which assists with gathering information on potential guests or topics for the podcast.

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    A Model Context Protocol server enabling advanced search and content extraction using the Tavily API, with rich customization and integration options.
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    57
    1
    JavaScript
    MIT License
  • Why this server?

    Enables automated browsing and form filling, which could be useful in gathering information from websites like LinkedIn.

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    Enables AI agents to interact with web browsers using natural language, featuring automated browsing, form filling, vision-based element detection, and structured JSON responses for systematic browser control.
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    23
    Python
    • Linux
    • Apple