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Why this server?
This server enables communication with users through Telegram, allowing you to ask questions to users and receive their responses via a Telegram bot, which would be necessary to obtain data from Telegram channels and groups.
Why this server?
Offers tools for analyzing text documents, including counting words and characters. This can be useful for analyzing the content of Telegram channels and groups.
Why this server?
Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes, allowing you to store insights discovered while analyzing Telegram data.
Why this server?
Allows accessing data accessible via JDBC such as Postgres, Oracle, mysql, mariadb, sqlite etc. Useful to store and analyze data extracted from Telegram.
Why this server?
Facilitates unified execution and result parsing for various testing frameworks, enabling validation and quality checks on data extracted or inferences made from telegram data.
Why this server?
A Model Context Protocol (MCP) server that provides tools for analyzing text documents, including counting words and characters. This server helps LLMs perform text analysis tasks by exposing simple document statistics functionality.
Why this server?
Leverages large language models to analyze users' WeGene genetic testing reports, providing access to report data via custom URI schemes and enabling profile and report management through OAuth authentication and API utilization.
Why this server?
This server provides a tool to generate unified diffs between two text strings, facilitating text comparison and analysis.
Why this server?
A Model Context Protocol (MCP) server implementation that provides the LLM an interface for visualizing data using Vega-Lite syntax.
Why this server?
A Model Context Protocol server that provides access to MongoDB databases. This server enables LLMs to inspect collection schemas and execute read-only queries.