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127,235 tools. Last updated 2026-05-05 11:43

"A tool for storing chat conversations and generating knowledge graphs" matching MCP tools:

  • Retrieve and filter knowledge graphs to obtain graph IDs and node information for subsequent operations like adding edges or querying node details.
    MIT
  • Add nodes to knowledge graphs for organizing components, events, requirements, or concepts. Supports multiple graph types including topology, timelines, and knowledge bases.
    MIT
  • Chat with local LLMs using conversation history, system messages, tool calling, and adjustable generation settings.
    AGPL 3.0
  • Retrieve recent Microsoft Teams conversations including 1:1 chats and group discussions with participant details and chat topics.
    MIT
  • Archive chat conversations in Open WebUI to organize and manage chat history, using chat IDs to identify specific conversations for archiving.
    MIT
  • Send chat messages to a vLLM server for multi-turn conversations with configurable model parameters and token limits.
    Apache 2.0

Matching MCP Servers

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    An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.
    Last updated
    12
  • A
    license
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    A server that enables users to chat with each other by repurposing the Model Context Protocol (MCP), designed for AI tool calls, into a human-to-human communication system.
    Last updated
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    MIT

Matching MCP Connectors

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  • The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.