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    An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.
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    Enables browser automation, web content extraction, and LLM-powered data transformation using Playwright. Supports session management, authentication flows, and works with local LLMs (Ollama, JAN AI) or external providers to clean and structure extracted web data.
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    MCP server that converts URLs into token-minimized clean text for LLMs, providing a receipt of token and cost savings.
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    MIT
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    A stdio MCP server that provides a single tool to query Gemini with Google Search grounding for current web information, returning synthesized answers with source links.
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    Read-only MCP server that lets AI agents safely query SQLite, PostgreSQL, and MySQL/MariaDB. Enforces read-only transactions with column masking, row caps, query timeouts, EXPLAIN-based cost rejection, and rate limiting.
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    This server enables AI systems to integrate with Tavily's search and data extraction tools, providing real-time web information access and domain-specific searches.
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    MIT
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    Integrates Jina.ai's Grounding API with LLMs for real-time, fact-based web content grounding and analysis, enhancing LLM responses with precise, verified information.
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    Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.
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    MIT
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    Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.
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    A Model Context Protocol (MCP) server that enables interaction with Waroom API to retrieve incident information and postmortem details through a standardized protocol.
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    MCP Ollama server integrates Ollama models with MCP clients, allowing users to list models, get detailed information, and interact with them through questions.
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    MIT
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    Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.
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    MIT
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    Enables querying Langfuse analytics, cost metrics, and usage data across multiple projects. Provides tools for trace analysis, model/service cost breakdowns, and daily usage trends through natural language queries.
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    MIT