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204,350 tools. Last updated 2026-06-15 00:11

"Information about sequential thinking or related topics" matching MCP tools:

  • Search the web for current information, news, articles, and websites to find up-to-date content, research topics, or answer questions about recent events.
    Apache 2.0
  • Retrieve comprehensive details about 3GPP specifications including metadata, content, dependencies, and related information to support telecommunications standards development.
    MIT
  • Search the web for current information using DuckDuckGo, returning summaries, related topics, and source URLs. No API key required. Privacy-first.
    Business Source 1.1
  • Search the web for current information, news, articles, and websites to answer questions about recent events, research topics, or find specific resources.
    Apache 2.0

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  • Retrieve detailed information about a prediction market event using its unique slug identifier. Events group related markets for streamlined analysis.
    MIT
  • Initiate structured reasoning sessions to analyze problems through sequential thinking steps, enabling systematic problem-solving with revision capabilities.
    MIT
  • Get detailed information about a running guide, including FAQs and related tools, by specifying the guide slug.
    MIT
  • Retrieve detailed information about any scholarly entity in OpenAlex by providing its entity type and identifier. Supports works, authors, sources, institutions, topics, publishers, and funders.
    MIT
  • Create execution plans with sequential thinking, risk analysis, and resource estimation to coordinate multi-agent workflows.
    MIT
  • Retrieve detailed information about all Kafka topics in a specified environment to manage and explore data across clusters.
    Apache 2.0
  • Generate sequential reasoning chains to structure complex thinking processes by breaking topics into logical steps for clearer analysis.
    MIT
  • Generate structured reasoning chains to break down complex topics into sequential thinking steps for clearer problem-solving and analysis.
    MIT
  • Retrieve stored information from long-term memory using semantic meaning, keywords, or both to provide context about topics when users ask questions.
    MIT
  • Process AntV-related queries by identifying, parsing, and structuring user requirements for visualization tasks. Extracts topics, detects intent, and prepares structured data for precise solutions.
    MIT
  • Retrieve detailed information about a specific Advisor recommendation, including impact level, likelihood, remediation steps, and related knowledge base articles for issue resolution.
    Apache 2.0