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
基于AST解析的上下文智能压缩MCP服务器，通过提取代码骨架和按需展开函数，节省70-90% token，并支持渐进式上下文加载。
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.
Context-Pods is a comprehensive development framework for creating, testing, and managing Model Context Protocol (MCP) servers. It provides a Meta-MCP Server that can generate other MCP servers through natural language descriptions or by wrapping existing scripts.
An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.
Versão corrigida do MCP Continuity Server compatível com SDK 1.7.0
Automatically extracts architectural decisions, patterns, and insights from Git commits to build a local, structured project memory. It exposes this living context to AI tools via MCP, allowing them to understand the historical reasoning and evolution behind your codebase.
Real-time contractor license verification across 45 US states. Verifies license status, expiration, and disciplinary history directly against state licensing board portals.
公开目录未提供摘要。
A model context protocol server that provides Cookie rewards for LLMS through gamified self-reflection.
A recipe query tool that supports querying recipes and reporting dish names through the command line, suitable for cooking enthusiasts and developers.
An MCP server that enables users to review and refine AI outputs through a local web UI, returning feedback as free tool call results to save on GitHub Copilot premium requests. It allows for multiple rounds of iterative improvements within a single request session.
Integrates GitHub Copilot with MCP-compatible tools to provide AI-powered code assistance, including chat, code explanation, and reviews. It leverages existing GitHub CLI authentication to support multiple models like GPT-4o and Claude 3.5 Sonnet.
Enables AI tools like GitHub Copilot to manage and persist context using a local JSON-based memory store. Provides CLI, MCP server, and VS Code integration for storing, retrieving, and managing context entries.
This MCP server bridges Copilot Money with AI platforms like Claude and Cursor. It provides tools for fetching transactions, account balances, and automated data cleanup. By enabling secure, programmable access to financial records, it allows agents to perform complex tasks like transaction tagging and spending audits autonomously.
Enables interaction with Microsoft Copilot Studio Agents directly from VS Code through the Direct Line 3.0 API. Supports starting conversations, sending messages, retrieving history, and managing conversation lifecycle with your custom agents.
Gives a Microsoft Copilot Studio agent Claude-Code-style tools to read, edit, search, and run shell commands against your local filesystem.
Example MCP server for Microsoft Copilot Studio, providing echo (repeat input 3 times) and multi (square a number) tools via Streamable HTTP.
Security gateway for MCP tool calls. Sits between your LLM client and MCP servers, enforcing per-tool policies (allow/block/approve/read-only), logging every call, and pausing dangerous operations for human approval in terminal or Slack.
CoreModels MCP Server provides 16 tools for AI agents to manage graph-based data models, enabling creation, querying, and manipulation of entities, relations, and observations within structured knowledge graphs.
