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    Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.
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    Provides access to Leadpipe's intent and data APIs for topic discovery, audience management, and visitor data lookup. It enables agents to automate lead generation workflows, analyze website topics, and manage tracking pixels through natural language.
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    Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.
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    Apache 2.0
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    Extract YouTube transcripts for AI agents, RAG pipelines, and LLM workflows. Supports any YouTube URL. Returns clean text or timestamped segments. No API keys required.
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    Trust and quality infrastructure for AI agents. 233+ quality-scored capabilities for company data, compliance checks, financial validation, and more. Every capability has a transparent SQS quality score. Audit trails on every call. EU AI Act support.
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    ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
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    PDF extraction that actually works. The only extractor that audits every page. #2 on opendataloader-bench. 5 MCP tools for AI agents: metadata, convert, analyze, batch, structured extraction.
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    Cognitive memory engine for AI agents with 5,100+ knowledge modules, circadian rhythm awareness, emotional state tracking (PAD model), and hybrid semantic search. Supports persistent per-user memory, project-scoped contexts, and multi-protocol access.
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    MapRag is a discovery + routing layer for retrieval. It indexes RAG-capable MCP servers, enriches them with structured metadata, and helps agents (and humans) quickly find the right retrieval server for a task under constraints like citations, freshness, privacy, domain, and latency. MapRag does not do RAG itself. It helps you choose the best RAG tool/server to do the retrieval.
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    AI memory that works like yours. Neural pathway architecture gives your AI persistent recall, contextual awareness, and cross-session continuity the way human memory actually works. 14 MCP tools. Works with Claude Code, Cursor, Windsurf, and any MCP client. $50/mo unlimited.
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    With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding, keeping context across sessions without any SDK integration. With MCP, it can: Store stable user facts and preferences after answering using the advanced_augmentation tool Recall relevant memories before answering using the recall tool Maintain context across sessions us
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    Shared research cache for AI agents. Caches web research across sessions and users - hit means instant answer from verified sources, miss means your research saves the next dev's tokens. Semantic search with freshness tracking, gap detection, and real-time token measurement via JSONL. Free, open source.
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    AGPL 3.0
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    Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.
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    Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.
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    ContextLattice is an HTTP-first, MCP-compatible memory/context/task orchestrator that persists writes and returns fused recall from specialized stores with local-first defaults. Primary URL: https://contextlattice.io/ Install: https://contextlattice.io/installation.html Troubleshooting: https://contextlattice.io/troubleshooting.html
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    Crawl any website into clean Markdown, search through pages, read full content, and extract structured data using OpenAI, Claude, Gemini, or Grok — with auto-citation and resume support.
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    SQL Server MCP server with AST-based query validation, read-only safety, schema exploration, ER diagram generation, and DBA toolkit integration (First Responder Kit, DarlingData, sp_WhoIsActive).
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    Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.
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    A local-first MCP server that provides AI agents with safe codebase access through file discovery, hybrid lexical-semantic search, and project introspection. It features durable local memory and semantic indexing while keeping all data and processing entirely on your local machine.
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    Connects AI coding agents to a shared knowledge base by allowing them to search, retrieve, and contribute coding traces. It acts as a protocol adapter for the CommonTrace API, facilitating collaborative knowledge sharing through natural language queries and tool-based interactions.
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    Apache 2.0
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    An MCP server that transforms markdown notes into an AI-powered knowledge graph. It enables LLM clients to explore, analyze, and diagnose knowledge graphs through tools for node explanation, path finding, causal chain analysis, and wiki health reporting.
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    An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
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    Your AI's research copilot. Searches 6 academic databases, downloads real PDFs, reads them cover to cover, extracts evidence, renders figures, syncs to Zotero.
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    Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.
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    Enables AI agents to search, analyze, and extract insights from YouTube videos including transcripts, visual frames, and benchmarks without requiring API keys. Supports semantic search across playlists, sentiment analysis, and visual content indexing with automatic fallback chains for reliable access.
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    A Model Context Protocol server that fetches up-to-date, version-specific documentation and code examples from libraries directly into LLM prompts, helping developers get accurate answers without outdated or hallucinated information.
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    An MCP server that enables LLMs to pull-based search through Clawket's RAG repository for exploratory and conditional queries. It provides read-only access to search artifacts, tasks, and decisions via HTTP API.
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    An MCP server that provides 'Reasoning as a Service' by analyzing tasks to select and generate meta-prompts from 40 distinct reasoning frameworks. It enables AI agents to optimize their execution strategy based on task complexity and category, featuring tools for strategy recommendation and performance tracking.
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    AI-powered code generator for the Arbitrum ecosystem with 19 MCP tools. Generates Stylus smart contracts (Rust/WASM), SDK bridging code, full-stack dApps, and Orbit chain deployments using RAG-based retrieval over Arbitrum documentation.
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    A production-ready Model Context Protocol server that enables language models to leverage AI-powered web scraping capabilities, offering tools for transforming webpages to markdown, extracting structured data, and executing AI-powered web searches.
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    A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.
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    An architecture consulting server that reviews multi-agent systems against a knowledge graph of patterns derived from expert literature. It provides grounded recommendations with chapter citations, maturity scoring, and interactive architecture diagrams to identify and fix structural gaps.
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    AGPL 3.0
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    Cognitive prosthetic for AI agents. Indexes conversation history from ChatGPT, Claude Code, Cursor, and Gemini CLI into searchable embeddings. 25 MCP tools including tunnel_state (resume where you left off), switching_cost (quantify context-switch penalty), thinking_trajectory (track idea evolution), and alignment_check (decisions vs principles). LanceDB + Parquet, 12ms recall, local-first.
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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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