Jupyter MCP Server alternatives

1

JupyterMCP

Enable direct Claude AI integration with Jupyter Notebook via the Model Context Protocol.

JupyterMCP integrates Jupyter Notebook (version 6.x) with Claude AI using the Model Context Protocol, enabling real-time interaction between AI models and live notebooks. It facilitates two-way communication, allowing Claude AI to execute code, manipulate cells, and retrieve outputs directly within the notebook. The system comprises a WebSocket server, client-side JavaScript, and an MCP server for seamless protocol implementation. Designed for developers and data scientists, it brings advanced AI assistance into interactive notebook environments.

111 22 MCP

2

MyMCP Server (All-in-One Model Context Protocol)

Powerful and extensible Model Context Protocol server with developer and productivity integrations.

MyMCP Server is a robust Model Context Protocol (MCP) server implementation that integrates with services like GitLab, Jira, Confluence, YouTube, Google Workspace, and more. It provides AI-powered search, contextual tool execution, and workflow automation for development and productivity tasks. The system supports extensive configuration and enables selective activation of grouped toolsets for various environments. Installation and deployment are streamlined, with both automated and manual setup options available.

93 9 MCP

3

Zettelkasten MCP Server

A Zettelkasten-based knowledge management system implementing the Model Context Protocol.

Zettelkasten MCP Server provides an implementation of the Zettelkasten note-taking methodology, enriched with bidirectional linking, semantic relationships, and categorization of notes. It enables creation, exploration, and synthesis of atomic knowledge using MCP for AI-assisted workflows. The system integrates with clients such as Claude and supports markdown, advanced search, and a structured prompt framework for large language models. The dual storage architecture and synchronous operation model ensure flexibility and reliability for managing personal or collaborative knowledge bases.

114 21 MCP

4

MCP Server for Data Exploration

Interactive Data Exploration and Analysis via Model Context Protocol

MCP Server for Data Exploration enables users to interactively explore and analyze complex datasets using prompt templates and tools within the Model Context Protocol ecosystem. Designed as a personal Data Scientist assistant, it facilitates the conversion of raw data into actionable insights without manual intervention. Users can load CSV datasets, run Python scripts, and generate tailored reports and visualizations through an AI-powered interface. The server integrates directly with Claude Desktop, supporting rapid setup and seamless usage for both macOS and Windows.

503 63 MCP

5

Lucidity MCP

Intelligent prompt-based code quality analysis for AI coding assistants.

Lucidity MCP is a Model Context Protocol (MCP) server that empowers AI coding assistants to deliver high-quality code through intelligent, prompt-driven analysis. It offers comprehensive detection of code issues across multiple quality dimensions, providing structured and actionable feedback. With language-agnostic capabilities, extensible framework, and flexible transport options, Lucidity MCP seamlessly integrates into developer workflows and AI systems.

72 20 MCP

6

MCP Atlassian

AI-powered MCP server integrating Confluence and Jira workflows.

MCP Atlassian serves as a Model Context Protocol (MCP) server interface for Atlassian products such as Confluence and Jira, supporting both cloud and server/data center deployments. It enables AI assistants to access, search, and update Atlassian data contextually via standardized MCP endpoints. The integration streamlines tasks like intelligent issue filtering, documentation creation, and context-driven updates directly through natural language. Multiple authentication modes, including API tokens and OAuth 2.0, are supported for secure connectivity.

3,574 740 MCP

7

FastMCP

The fast, Pythonic way to build MCP servers and clients.

FastMCP is a production-ready framework for building Model Context Protocol (MCP) applications in Python. It streamlines the creation of MCP servers and clients, providing advanced features such as enterprise authentication, composable tools, OpenAPI/FastAPI generation, server proxying, deployment tools, and comprehensive client libraries. Designed for ease of use, it offers both standard protocol support and robust utilities for production deployments.

20,201 1,488 MCP

8

Jira MCP

AI-Ready Jira Server for Real-World Developer Workflows

Jira MCP is an opinionated server implementation of the Model Context Protocol (MCP) tailored for Jira automation and integration. It provides advanced tools to manage Jira issues, sprints, development data, and project versions, reflecting workflows of real engineering and QA teams. Designed for seamless AI and tool integrations, it supports context-rich retrieval of Jira information for enhancing development transparency and automation.

69 19 MCP

9

Last9 MCP Server

Enables AI agents to access real-time production observability data for automated code fixes.

Last9 MCP Server is an implementation of the Model Context Protocol (MCP) designed to provide seamless integration between AI agents and production observability data. It allows tools and agents to fetch live logs, metrics, traces, events, and alerts from Last9 systems, supporting a range of development environments and IDEs. This enables faster debugging, automated code fixes, and insightful context directly within local development workflows.

46 8 MCP

10

MCP Obsidian Server

Integrate Obsidian note management with AI models via the Model Context Protocol.

MCP Obsidian Server acts as a bridge between Obsidian and AI models by providing an MCP-compatible server interface. It enables programmatic access to Obsidian vaults through a local REST API, allowing operations like listing files, searching, reading, editing, and deleting notes. Designed to work with Claude Desktop and other MCP-enabled clients, it exposes a set of tools for efficient note and content management within Obsidian.

2,394 298 MCP

11

Modbus MCP Server

Standardizes Modbus data for seamless AI integration via the Model Context Protocol.

Modbus MCP Server provides an MCP-compliant interface that standardizes and contextualizes Modbus device data for use with AI agents and industrial IoT systems. It supports flexible Modbus connections over TCP, UDP, or serial interfaces and offers a range of Modbus tools for reading and writing registers and coils. With customizable prompts and structured tool definitions, it enables natural language-driven interactions and analysis of Modbus data within AI workflows. The solution is designed to ensure interoperability and easy configuration within MCP-compatible environments.

18 7 MCP

12

Godot MCP

A Model Context Protocol (MCP) server implementation using Godot and Node.js.

Godot MCP implements the Model Context Protocol (MCP) as a server, leveraging the Godot game engine along with Node.js and TypeScript technologies. Designed for seamless integration and efficient context management, it aims to facilitate standardized communication between AI models and applications. This project offers a ready-to-use MCP server for developers utilizing Godot and modern JavaScript stacks.

1,071 124 MCP

13

Markdownify MCP Server

Convert diverse files and web content into Markdown via the Model Context Protocol.

Markdownify MCP Server offers a protocol-based server that transforms various file types—including PDF, images, audio, DOCX, XLSX, and PPTX—as well as web content like YouTube videos, Bing search results, and web pages into Markdown format. The server exposes a suite of conversion tools through a standardized interface for easy integration with applications. Optional configuration allows retrieval of Markdown files from restricted directories, and the platform supports development customization for additional tool integration. Deployment and operation are straightforward with cross-platform support (with pending Windows improvements).

2,256 188 MCP

14

GitLab MR MCP

An MCP server for AI-powered interaction with GitLab merge requests and issues.

GitLab MR MCP implements a server for the Model Context Protocol (MCP) enabling AI agents to perform structured interactions with GitLab repositories. It provides capabilities for reading merge request and issue details, listing projects, fetching diffs and comments, and making changes such as commenting or updating titles and descriptions. Installation is supported via Smithery or manually, and integration with MCP clients and Inspector tools is documented for development and troubleshooting.

65 19 MCP

15

Optuna MCP Server

Automated model optimization and analysis via the Model Context Protocol using Optuna.

Optuna MCP Server is an implementation of the Model Context Protocol (MCP) that enables automated hyperparameter optimization and analysis workflows through Optuna. It acts as a server providing standardized tools and endpoints for creating studies, managing trials, and visualizing optimization results. The server facilitates integration with MCP clients and supports deployment via both Python environments and Docker. It streamlines study creation, metric management, and result handling using Optuna’s capabilities.

65 21 MCP

16

Maven Tools MCP Server

Universal Maven Central dependency intelligence server for JVM build tools via the Model Context Protocol.

Maven Tools MCP Server provides an MCP-compliant API delivering rich Maven Central dependency intelligence for JVM build tools like Maven, Gradle, SBT, and Mill. It enables AI assistants to instantly analyze, interpret, and recommend updates, health checks, and maintenance insights by reading maven-metadata.xml directly from Maven Central. With Context7 integration, it supports orchestration and documentation, enabling bulk analysis, stable version filtering, risk assessment, and rapid cached responses. Designed for seamless integration into AI workflows via the Model Context Protocol.

14 2 MCP

17

MXCP

Enterprise-Grade Model Context Protocol Framework for AI Applications

MXCP is an enterprise-ready framework that implements the Model Context Protocol (MCP) for building secure, production-grade AI application servers. It introduces a structured methodology focused on data modeling, robust service design, policy enforcement, and comprehensive testing, integrated with strong security and audit capabilities. The framework enables rapid development and deployment of AI tools, supporting both SQL and Python environments, with built-in telemetry and drift detection for reliability and compliance.

49 6 MCP

18

Apple Books MCP

Model Context Protocol server for integrating and managing Apple Books data with AI assistants.

Apple Books MCP is a server implementation of the Model Context Protocol (MCP) that enables AI assistants to access, organize, and interact with Apple Books data such as collections, books, highlights, notes, and annotations. It provides standardized tools for retrieving and searching book-related data, supporting integration with platforms like Claude Desktop. Designed for easy setup with Python or uv, it facilitates context-aware AI capabilities for reading and annotation workflows.

33 5 MCP

19

anki-mcp

MCP server for seamless integration with Anki via AnkiConnect.

An MCP server that bridges Anki flashcards with the Model Context Protocol, exposing AnkiConnect functionalities as standardized MCP tools. It organizes Anki actions into intuitive services covering decks, notes, cards, and models for easy access and automation. Designed for integration with AI assistants and other MCP-compatible clients, it enables operations like creating, modifying, and organizing flashcards through a unified protocol.

6 4 MCP

20

GIS MCP Server

Empower AI with advanced geospatial operations via Model Context Protocol.

GIS MCP Server provides a Model Context Protocol (MCP) server implementation that enables Large Language Models to access and perform sophisticated GIS operations. It bridges AI assistants with Python geospatial libraries such as Shapely, GeoPandas, PyProj, Rasterio, and PySAL. The server supports a wide range of spatial analysis, coordinate transformations, raster and vector data processing, and geospatial intelligence tasks. By integrating with MCP-compatible clients, it enhances AI tools with precise and extensible spatial capabilities.

70 21 MCP

21

ScreenMonitorMCP v2

Real-time screen monitoring and visual analysis for AI assistants via MCP.

ScreenMonitorMCP v2 is a Model Context Protocol (MCP) server enabling AI assistants to capture, analyze, and interact with screen content in real time. It supports instant screenshots, live streaming, advanced vision-based analysis, and provides performance monitoring across Windows, macOS, and Linux. Integration with clients like Claude Desktop is streamlined, offering easy configuration and broad compatibility. The tool leverages AI vision models to provide intelligent insights into screen content and system health.

64 14 MCP

22

Context Apps MCP

AI-powered productivity suite unified by the Model Context Protocol.

Context Apps MCP is an AI-powered productivity suite that connects Todo, Idea, Journal, and Timer applications through the Model Context Protocol, enabling smart, context-driven workflows with apps like Claude Desktop and other MCP-compatible clients. It allows users to manage tasks, develop ideas, analyze journals, and optimize productivity using AI, all accessible and integrated via MCP-compliant endpoints. The suite ensures secure authentication via Apple ID OAuth, provides end-to-end encrypted communication, and offers users complete control over their data.

5 2 MCP

23

Markmap MCP Server

Convert Markdown to interactive mind maps via the Model Context Protocol.

Markmap MCP Server enables seamless conversion of Markdown content into interactive mind maps using the Model Context Protocol (MCP). It leverages the open-source markmap project and provides users with diverse export formats including PNG, JPG, and SVG. Designed for easy integration with MCP clients, it offers tools for automated browser previews, rich interactivity, and batch mind map generation. The server can be installed easily via npm or Smithery and supports configurable output directories.

137 22 MCP

24

cryptopanic-mcp-server

Provides real-time cryptocurrency news to AI agents via Model Context Protocol.

cryptopanic-mcp-server delivers up-to-date cryptocurrency news to AI agents by integrating with the CryptoPanic API. It exposes an MCP-compliant server with a tool for fetching news or media headlines, supporting customization via API keys and configuration files. The implementation is intended for streamlined context integration into AI workflows, enhancing agent awareness with current crypto market updates.

59 23 MCP

25

Ebook-MCP

A Model Context Protocol server for conversational e-book interaction and AI integration.

Ebook-MCP acts as a Model Context Protocol (MCP) server enabling seamless interaction between large language model (LLM) applications and electronic books such as EPUB and PDF. It standardizes APIs for AI-powered reading, searching, and managing digital libraries. Through natural language interfaces, it provides smart library management, content navigation, and interactive learning within digital books. Ebook-MCP integrates with modern AI-powered IDEs and supports multi-format digital book processing.

132 23 MCP

26

Label Studio MCP Server

Bridge between Model Context Protocol clients and Label Studio for project, task, and prediction management.

Label Studio MCP Server implements a Model Context Protocol (MCP) interface to enable programmatic management of Label Studio projects, tasks, and model predictions. It enables structured interactions with a running Label Studio instance via MCP clients, supports various project and task operations, and integrates the official Label Studio SDK. The tool exposes multiple functions for project management, task handling, and prediction integration, allowing both natural language and structured API calls.

23 8 MCP

27

OPC UA MCP Server

Bridge AI agents with OPC UA industrial systems in real time.

OPC UA MCP Server enables seamless integration of AI agents with OPC UA-enabled industrial automation systems. It provides real-time monitoring, analysis, and control of operational data through a set of standardized tool APIs. Supporting both reading and writing of OPC UA nodes, the server facilitates natural language interaction by exposing tools for AI-driven automation and control workflows.

20 8 MCP

28

Daisys MCP server

A beta server implementation for the Model Context Protocol supporting audio context with Daisys integration.

Daisys MCP server provides a beta implementation of the Model Context Protocol (MCP), enabling seamless integration between the Daisys AI platform and various MCP clients. It allows users to connect MCP-compatible clients to Daisys by configurable authentication and environment settings, with out-of-the-box support for audio file storage and playback. The server is designed to be extensible, including support for both user-level deployments and developer contributions, with best practices for secure authentication and dependency management.

10 5 MCP

29

YAMCP

Unified management and orchestration of Model Context Protocol servers via a local gateway workspace.

YAMCP is a command-line tool that enables organizing and managing multiple MCP (Model Context Protocol) servers as unified workspaces. It allows users to connect to various local or remote MCP servers, group them by functionality or application, and expose them as a single MCP server gateway for AI applications. The tool simplifies monitoring and debugging by centralizing server communication logs and provides workspace management and runtime control. YAMCP facilitates seamless integration with AI applications by providing a consolidated configuration and context management interface.

57 9 MCP

30

ws-mcp

WebSocket bridge for MCP stdio servers.

ws-mcp wraps Model Context Protocol (MCP) stdio servers with a WebSocket interface, enabling seamless integration with web-based clients and tools. It allows users to configure and launch multiple MCP servers via a flexible configuration file or command-line arguments. The tool is designed to be compatible with services such as wcgw, fetch, and other MCP-compliant servers, providing standardized access to system operations, HTTP requests, and more. Integration with tools like Kibitz enables broader applications in model interaction workflows.

19 7 MCP

31

youtube_mcp

YouTube transcription server with Model Context Protocol integration.

youtube_mcp is a server that enables transcription of YouTube videos using the Model Context Protocol (MCP). Users can set up the server, authenticate via an MCP key, and stream video transcript data for integrated AI tools. Designed for use with applications like Claude desktop, it supports OpenAI APIs and utilizes browser cookies for access.

26 3 MCP

32

Aviationstack MCP Server

MCP server offering comprehensive endpoints for aviation and flight data.

Aviationstack MCP Server provides an MCP-compliant API that exposes tools to access real-time and scheduled flight data, aircraft details, random aircraft types, countries, and city information from the AviationStack API. It offers ready-to-use endpoints for airline-specific flight queries, airport schedules, and in-depth vehicle, country, and city data. The solution applies the Model Context Protocol by defining MCP tools as Python functions with standardized interfaces, designed for seamless integration into MCP-compatible environments. The server is built using Python, incorporates the FastMCP library, and is intended for easy deployment and use in application development.

11 4 MCP

33

Catalysis Hub MCP Server

MCP-compliant server for accessing catalysis research data via GraphQL.

Catalysis Hub MCP Server provides a Model Context Protocol interface to the Catalysis Hub's GraphQL API, enabling seamless programmatic access to catalyst research datasets. It allows users to execute flexible and complex GraphQL queries to retrieve reaction, material, and publication data. The server supports robust error handling, parameterized queries, and is designed for interoperability with AI agents in scientific workflows.

1 3 MCP

34

interactive-mcp

Enable interactive, local communication between LLMs and users via MCP.

interactive-mcp implements a Model Context Protocol (MCP) server in Node.js/TypeScript, allowing Large Language Models (LLMs) to interact directly with users on their local machine. It exposes tools for requesting user input, sending notifications, and managing persistent command-line chat sessions, facilitating real-time communication. Designed for integration with clients like Claude Desktop and VS Code, it operates locally to access OS-level notifications and command prompts. The project is suited for interactive workflows where LLMs require user involvement or confirmation.

313 25 MCP

35

MetaTrader MCP Server

Let AI assistants trade for you using natural language.

MetaTrader MCP Server is a bridge that connects AI assistants such as Claude and ChatGPT to the MetaTrader 5 trading platform via the Model Context Protocol (MCP). It enables users to perform trading actions on MetaTrader 5 through natural language instructions. The system supports real-time data access, full account management, and secure local credential handling, offering both MCP and REST API interfaces.

120 34 MCP

36

IP Geolocation MCP Server

Enables IP-based geolocation for AI workflows via MCP-compliant tooling.

IP Geolocation MCP Server provides a Model Context Protocol (MCP) server that integrates with the ipinfo.io API to fetch detailed IP address information. It allows obtaining location, organization, and network details about users, facilitating context-aware AI applications. The tool can be seamlessly installed or integrated in client environments supporting MCP, with a focus on simplicity and developer-friendliness.

36 13 MCP

37

Jotdown

MCP Server for Notion Page Creation and mdBook Generation

Jotdown is an MCP server enabling large language models to interact seamlessly with Notion and generate markdown books (mdBooks). It allows LLMs to create or update Notion pages and manage the entire process of markdown book creation, including structure and navigation. Leveraging the Model Context Protocol, it provides tools for consistent and intelligent context handling between LLMs and external content platforms.

19 4 MCP

38

WebScraping.AI MCP Server

MCP server for advanced web scraping and AI-driven data extraction

WebScraping.AI MCP Server implements the Model Context Protocol to provide web data extraction and question answering functionalities. It integrates with WebScraping.AI to offer robust tools for retrieving, rendering, and parsing web content, including structured data and natural language answers from web pages. It supports JavaScript rendering, proxy management, device emulation, and custom extraction configurations, making it suitable for both individual and team deployments in AI-assisted workflows.

33 13 MCP

39

Web3 Jobs MCP Server

AI-ready MCP server delivering curated, real-time Web3 job listings.

Web3 Jobs MCP Server provides real-time access to curated Web3 job listings from web3.career through a Model Context Protocol (MCP) interface, enabling intelligent job discovery for AI agents. The server supports advanced filtering by location, remote status, job tag, and result limits, and returns results in Markdown format. It enables the generation of natural language prompts for job searches, facilitating seamless integration with AI platforms such as Claude Desktop. Designed for Python 3.10+, it offers flexible deployment and configuration options.

4 3 MCP

40

mcp-kibela

MCP server for secure, AI-assisted access to Kibela notes.

mcp-kibela is a Model Context Protocol (MCP) server implementation that enables AI assistants to search, retrieve, create, and update content from Kibela, a team knowledge-sharing platform. It provides standardized endpoints for note search, fetching individual or recent notes, and managing note content. Integrating with tools like Claude, Cursor, and VSCode, it allows seamless, secure access to organizational knowledge through MCP-enabled clients. Authentication via environment variables ensures secure connections to Kibela APIs.

12 7 MCP

41

PDF Tools MCP

Comprehensive PDF manipulation via MCP protocol.

PDF Tools MCP provides an extensive suite of PDF manipulation operations using the Model Context Protocol framework. It supports both local and remote PDF tasks, such as rendering pages, merging, extracting metadata, retrieving text, and combining documents. The tool registers endpoints through the MCP protocol, enabling seamless server-based PDF processing for various clients. Built with Python, it emphasizes secure handling and compatibility with Claude Desktop via the Smithery ecosystem.

31 8 MCP

42

GitHub Repos Manager MCP Server

Token-based, high-performance GitHub automation for MCP clients.

GitHub Repos Manager MCP Server offers a comprehensive Model Context Protocol (MCP) server for managing GitHub repositories via direct API integration. It enables MCP clients such as Claude Desktop, Roo Code, and Cursor to automate GitHub workflows using just a personal access token, without the need for Docker. With 89 tools, users can handle repository management, issue tracking, collaboration, and advanced configuration with fine-grained control. The server is optimized for simplicity, security, and powerful automation through flexible tool management and granular access permissions.

13 4 MCP

43

Context7 MCP

Up-to-date code docs for every AI prompt.

Context7 MCP delivers current, version-specific documentation and code examples directly into large language model prompts. By integrating with model workflows, it ensures responses are accurate and based on the latest source material, reducing outdated and hallucinated code. Users can fetch relevant API documentation and examples by simply adding a directive to their prompts. This allows for more reliable, context-rich answers tailored to real-world programming scenarios.

36,881 1,825 MCP

44

Zaturn

Your Co-Pilot For Data Analytics & Business Insights

Zaturn enables AI models to interact with multiple data sources and generate analytics without requiring users to write SQL or Python code. It supports SQL databases and file formats, providing visual and tabular summaries and offering an interactive web interface similar to Jupyter Notebook. Zaturn can function both as a standalone platform or as a Model Context Protocol (MCP) compliant tool, allowing seamless context management for AI-driven data analysis.

66 9 MCP

45

Crypto Trending MCP Server

Real-time trending cryptocurrency data via Model Context Protocol tools.

Crypto Trending MCP Server tracks and monitors the latest trending tokens on CoinGecko, providing real-time insights into the most popular cryptocurrencies. It exposes MCP tools and prompts, such as a Markdown generator for trending token data and a parser prompt for extracting and analyzing the results with AI models. The solution is built for seamless integration with clients like Claude Desktop and allows natural language querying of current crypto trends. Its lightweight and extensible design supports easy deployment and extension.

6 5 MCP

46

MCP-Human

Enabling human-in-the-loop decision making for AI assistants via the Model Context Protocol.

MCP-Human is a server implementing the Model Context Protocol that connects AI assistants with real human input on demand. It creates tasks on Amazon Mechanical Turk, allowing humans to answer questions when AI systems require assistance. This solution demonstrates human-in-the-loop AI by providing a bridge between AI models and external human judgment through a standardized protocol. Designed primarily as a proof-of-concept, it can be easily integrated with MCP-compatible clients.

20 3 MCP

47

FastMCP LaTeX Server (tex-mcp)

A microservice for rendering LaTeX and templates to PDF via the Model Context Protocol.

FastMCP LaTeX Server is a microservice designed to render raw LaTeX or Jinja2 template documents and compile them into PDF files. It exposes a suite of five MCP-compatible tools for document rendering, template management, and artifact handling. The service can be run locally or integrated seamlessly with Claude Desktop, supporting both stdin/stdout MCP communication and HTTP-based transport. Output artifacts, including .tex and .pdf files, are generated in a dedicated directory, and a variety of ready-made templates are provided for easy customization.

4 2 MCP

48

MarkItDown

Convert diverse files into Markdown for seamless LLM integration.

MarkItDown is a lightweight Python utility for converting a wide range of file types—including PDF, Office documents, images, audio, websites, and more—into structured Markdown optimized for language models and text analysis tools. It includes an implementation of the Model Context Protocol (MCP) to facilitate integration with LLM applications, such as Claude Desktop. MarkItDown supports context-aware document conversions, prioritizing preservation of hierarchy and meaningful content, and can be used via CLI or as a library.

82,918 4,694 MCP

49

GrowthBook MCP Server

Interact with GrowthBook from your LLM client via MCP.

GrowthBook MCP Server enables seamless integration between GrowthBook and LLM clients by implementing the Model Context Protocol. It allows users to view experiment details, add feature flags, and manage GrowthBook configurations directly from AI applications. The server is configurable via environment variables and leverages GrowthBook's API for functionality. This integration streamlines experimentation and feature management workflows in AI tools.

15 12 MCP

50

MCP Unity Editor

A Model Context Protocol implementation for Unity game development.

MCP Unity Editor implements the Model Context Protocol within the Unity Editor environment, enabling standardized management of model context. It provides tools and infrastructure for developers to build and integrate AI and model-driven workflows in Unity projects. Built with support for Unity and Node.js, it aims to facilitate robust context handling and protocol compliance for model interactions.

1,075 131 MCP

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