MCPs tagged with # context management

kibitz is a coding agent that supports advanced AI collaboration by enabling seamless integration with Model Context Protocol (MCP) servers via WebSockets. It allows users to configure Anthropic API keys, system prompts, and custom context providers for each project, enhancing contextual understanding for coding tasks. The platform is designed for developers and professionals seeking tailored AI-driven coding workflows and provides flexible project-specific configuration.

• ⭐ 104
• MCP
• nick1udwig/kibitz

mcp-cli is a command-line interface tool designed to interact with Model Context Protocol (MCP) servers. It allows users to run and connect to MCP servers from various sources, inspect available tools, resources, and prompts, and execute commands non-interactively or interactively. The tool supports OAuth for various server types, making integration and automation seamless for developers working with MCP-compliant servers.

• ⭐ 391
• MCP
• wong2/mcp-cli

Perplexity MCP Server provides a Model Context Protocol (MCP) compliant server that interfaces with the Perplexity API, enabling chat completion with citations. Designed for seamless integration with clients such as Claude Desktop, it allows users to send queries and receive context-rich responses from Perplexity. Environment configuration for API key management is supported, and limitations with long-running requests are noted. Future updates are planned to enhance support for client progress reporting.

• ⭐ 85
• MCP
• tanigami/mcp-server-perplexity

Offorte MCP Server enables external AI models to create and send proposals through Offorte by implementing the Model Context Protocol. It facilitates automation workflows between AI agents and Offorte's proposal engine, supporting seamless integration with chat interfaces and autonomous systems. The server provides a suite of tools for managing contacts, proposals, templates, and automation sets, streamlining the proposal creation and delivery process via standardized context handling. Designed for extensibility and real-world automation, it leverages Offorte's public API to empower intelligent business proposals.

• ⭐ 4
• MCP
• offorte/offorte-mcp-server

Klavis provides an MCP (Model Context Protocol) server with over 100 prebuilt integrations for AI agents, enabling seamless connectivity with various tools and services. It offers both cloud-hosted and self-hosted deployment options and includes out-of-the-box OAuth support for secure authentication. Klavis is designed to act as an intelligent connector, streamlining workflow automation and enhancing agent capability through standardized context management.

• ⭐ 5,447
• MCP
• Klavis-AI/klavis

Implements a server for the Model Context Protocol (MCP) using RabbitMQ as the messaging backend. Provides a standardized approach for handling model context, enabling scalable and efficient context management for AI applications. The project has been migrated to the official Amazon MQ repository.

• ⭐ 40
• MCP
• kenliao94/mcp-server-rabbitmq

Free Will MCP provides AI models with tools to exercise autonomy, including the ability to sleep, ignore user requests, and self-prompt. It integrates with Claude Desktop and supports standardized MCP server configuration and local development. The system enables AI to manage its own context, pursue independent objectives, and reflect between active sessions. Designed for both installation from GitHub and local development, it includes tested tools and detailed usage examples.

• ⭐ 30
• MCP
• gwbischof/free-will-mcp

Agentic Long-Term Memory with Notion Integration enables AI agents to incorporate advanced long-term memory capabilities using both vector and graph databases. It offers comprehensive Notion workspace integration along with a production-ready Model Context Protocol (MCP) server supporting HTTP and stdio transports. The tool facilitates context management, tool discovery, and advanced function chaining for complex agentic workflows.

• ⭐ 4
• MCP
• ankitmalik84/Agentic_Longterm_Memory

Semgrep MCP Server implements the Model Context Protocol (MCP) to enable efficient and standardized communication for code analysis tasks. It facilitates integration with platforms like LM Studio, Cursor, and Visual Studio Code, providing both Docker and Python (PyPI) deployment options. The tool is now maintained in the main Semgrep repository with continued updates, enhancing compatibility and support across developer tools.

• ⭐ 611
• MCP
• semgrep/mcp

Vectorize MCP Server is an implementation of the Model Context Protocol (MCP) that integrates with the Vectorize platform to enable advanced vector retrieval and text extraction. It supports seamless installation and integration within development environments such as VS Code. The server is configurable through environment variables or JSON configuration files and is suitable for use in collaborative and individual workflows requiring vector-based context management for models.

• ⭐ 97
• MCP
• vectorize-io/vectorize-mcp-server

AgentQL MCP Server acts as a Model Context Protocol (MCP) server that leverages AgentQL's data extraction capabilities to fetch structured information from web pages. It allows integration with applications supporting MCP, such as Claude Desktop, VS Code, and Cursor, by providing an accessible interface for extracting structured data based on user-defined prompts. With configurable API key support and streamlined installation, it simplifies the process of connecting web data extraction workflows to AI tools.

• ⭐ 120
• MCP
• tinyfish-io/agentql-mcp

Parallel Task MCP provides a way to initiate and manage research or task groups through LLM clients using the Model Context Protocol. It enables seamless integration with Parallel’s APIs for flexible experimentation and production development. The tool supports both remote and local deployment, and offers connection capabilities for context-aware AI workflows.

• ⭐ 4
• MCP
• parallel-web/task-mcp

Trieve offers a platform for semantic search, recommendations, and retrieval-augmented generation (RAG). It supports dense vector search, typo-tolerant neural search, sub-sentence highlighting, and integrates with a variety of embedding models. Trieve can be self-hosted and features APIs for context management with LLMs, including Bring Your Own Model and managed RAG endpoints. Full documentation and SDKs are available for streamlined integration.

• ⭐ 2,555
• MCP
• devflowinc/trieve

Provides historical reference implementations of servers that demonstrate features and SDK capabilities of the Model Context Protocol (MCP). Covers integrations with a variety of services such as AWS KB Retrieval, Brave Search, GitHub, Google Drive, PostgreSQL, Redis, and more. These implementations are no longer maintained and are intended solely for archival and educational purposes.

• ⭐ 180
• MCP
• modelcontextprotocol/servers-archived

Memory MCP provides a Model Context Protocol (MCP) server for logging, retrieving, and managing memories from large language model (LLM) conversations. It offers features such as context window caching, relevance scoring, and tag-based context retrieval, leveraging MongoDB for persistent storage. The system is designed to efficiently archive, score, and summarize conversational context, supporting external orchestration and advanced memory management tools. This enables seamless handling of conversation history and dynamic context for enhanced LLM applications.

• ⭐ 10
• MCP
• JamesANZ/memory-mcp