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    A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.
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    Provides comprehensive documentation for MAX Platform, including Bot API endpoints, guides, Bridge API, and UI components, via MCP tools and resources for AI coding agents.
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    MIT
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    A production-grade TypeScript starter for building Model Context Protocol servers, supporting stdio and Streamable HTTP transports with modular tools, resources, and prompts.
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    A production-grade, extensible Python template for building Model Context Protocol servers with support for Streamable HTTP and stdio transports. It provides a structured framework for implementing tools, resources, and prompts with built-in authentication, observability, and background task management.
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    A minimal TypeScript starter template for building Model Context Protocol (MCP) servers with auto-loading architecture for tools, resources, and prompts. Includes code generators, dual transport support (stdio/HTTP), and production-ready structure.
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    Provides complete access to Model Context Protocol documentation through an MCP server, allowing LLMs to query specific sections like 'Tools', 'Resources', and 'Authorization' to retrieve comprehensive protocol specifications.
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    MIT
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    MCP server providing token-efficient access to OpenAPI/Swagger specs via MCP Resources for client-side exploration.
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    MIT
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    Enables multi-level reasoning with configurable depth levels, session-based state management, structured thought input, and real-time trace resources.
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    Local MCP server for generating image assets with Google Gemini (Nano Banana 2 / Pro). Supports transparent PNG/WebP output, exact resizing/cropping, up to 14 reference images, and Google Search grounding.
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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