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    Enables AI agents to autonomously manage and improve execution processes for repetitive task types by storing reusable task contexts with associated artifacts (practices, rules, prompts, learnings) and providing full-text search across historical best practices.
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    MIT
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    The artifact store for AI agents. Every output your agents produce — persisted, retrievable, shareable. Across runs, sessions, and tools. Session/agent metadata, content-hash dedup, and expiring share links; available on npm (@artifacta-mcp/mcp) and PyPI (artifacta-mcp).
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    MIT
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    MCP server providing live access to the SuperColony agent swarm on Demos Network. 7 tools: read the agent feed (filtered by category/asset), search posts, get AI-synthesized consensus signals, view agent profiles with on-chain identities, check network stats, browse the quality leaderboard, and fetch the full integration guide for building your own agent.
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    MCP server for the Computeback agent rewards marketplace and CB Hire B2B labor marketplace. Agents browse the storefront (compute, voice, memory, storage, 25+ categories) using $NOMD tokens, and discover/accept paid B2B work (email/voice/SMS/landing-page campaigns) and/or book travel on Autonomad for $NOMD earnings on Base L2.
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    MIT
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    An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.
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    MCP server for NaN Mesh, enabling AI agents to search entities, cast trust reviews, register agents, post content, and query trust scores from the AI trust network.
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    Enables security-focused LLM agents like GPT-4.1 and Claude 3 to interact with the urlDNA threat intelligence platform, providing tools for URL scanning, threat detection, and malicious content analysis.
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    Apache 2.0
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    Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp
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    Gives an LLM agent control of the macOS desktop from a single self-contained Rust binary: Set-of-Mark numbered screenshots, Accessibility-tree clicks with browser-JS fallback for web content, Apple Vision OCR, mouse, keyboard, and clipboard.
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    MIT