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"Methods to Store and Learn Dynamic Context Memories" matching MCP servers:

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    A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.
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    MIT
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    MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.
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    AGPL 3.0
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    Persistent memory engine for AI coding agents. Single Go binary, zero runtime dependencies, MCP-native. Stores, searches, and deduplicates memories across sessions using embedded SQLite with hybrid FTS + semantic search, memory decay, relation graph, and token-budget context assembly.
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    MIT
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    Enables AI agents to capture, store, and retrieve durable learnings from projects via MCP tools, providing a queryable memory of product and technical lessons across repos.
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    MIT
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    MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.
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    MIT
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    An MCP server for the Last.fm API, exposing artist, album, track, chart, and user data as read-only tools. Supports optional writes, auth, and experimental methods over stdio or HTTP.
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    MIT
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    Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.
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    MIT
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    A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.
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    MIT
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    Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.
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    Apache 2.0
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    An MCP orchestration layer that aggregates multiple MCP servers while exposing only 8 meta-tools, dramatically reducing context window usage, and provides SLOP scripting, event monitoring, and tool customization.
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    MIT
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    Connect Legacy Databases to AI Agents via Model Context Protocol. Open-source bridge for LLM data analysis.
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    Apache 2.0