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"Database MCP (Model Context Protocol) Server" matching MCP servers:

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    Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.
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    MIT
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    MCP server that pings 130+ free coding LLM models across 17 providers in real-time, ranks them by latency, and helps AI agents pick the fastest available model.
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    MIT
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    An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.
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    MIT
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    Enables cross-chain bridging, multi-chain token swaps, and real-time pricing across over 50 blockchain networks using the Relay Protocol REST API. It allows users to browse thousands of tokens, generate quotes, and monitor transaction status directly through natural language.
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    MIT
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    Security-enforcing MCP proxy that sits between an AI agent and any number of downstream MCP servers, intercepting every tool call through a capability-token policy gateway that can allow, deny, or escalate to human approval before the call reaches any real tool. It also exposes built-in operator tools for approval workflows, audit trail queries, token management, voice/HUD output, and hierarchical
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    Apache 2.0
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    A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.
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    MIT
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    An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.
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    Elastic 2.0
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    Queries Claude Sonnet 4.6 and OpenAI GPT-5.5 in parallel via OpenRouter, returning structured multi-perspective responses for comparison and cross-checking.
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    MIT
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    Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.
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    MIT