locust-mcp-server alternatives

1

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

2

MCP CLI

A powerful CLI for seamless interaction with Model Context Protocol servers and advanced LLMs.

MCP CLI is a modular command-line interface designed for interacting with Model Context Protocol (MCP) servers and managing conversations with large language models. It integrates with the CHUK Tool Processor and CHUK-LLM to provide real-time chat, interactive command shells, and automation capabilities. The system supports a wide array of AI providers and models, advanced tool usage, context management, and performance metrics. Rich output formatting, concurrent tool execution, and flexible configuration make it suitable for both end-users and developers.

1,755 299 MCP

3

MCP Server for ZenML

Expose ZenML data and pipeline operations via the Model Context Protocol.

Implements a Model Context Protocol (MCP) server for interfacing with the ZenML API, enabling standardized access to ZenML resources for AI applications. Provides tools for reading data about users, stacks, pipelines, runs, and artifacts, as well as triggering new pipeline runs if templates are available. Includes robust testing, automated quality checks, and supports secure connection from compatible MCP clients. Designed for easy integration with ZenML instances, supporting both local and remote ZenML deployments.

32 10 MCP

4

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

5

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

6

Parallel Task MCP

Launch deep research or task groups for Parallel APIs via the Model Context Protocol.

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 3 MCP

7

nerve

The Simple Agent Development Kit for LLM-based automation with native MCP support

Nerve provides a platform for building, running, evaluating, and orchestrating large language model (LLM) agents using declarative YAML configurations. It supports both client and server roles for the Model Context Protocol (MCP), allowing seamless integration, team collaboration, and advanced agent orchestration. With extensible tool support, benchmarking, and LLM-agnostic handling via LiteLLM, it enables programmable and reproducible workflows for technical users.

1,278 109 MCP

8

Jupyter MCP Server

Real-time, context-aware MCP server for managing and interacting with Jupyter Notebooks.

Jupyter MCP Server is an implementation of the Model Context Protocol (MCP) designed to enable AI-driven, real-time management and interaction with Jupyter Notebooks. It offers context-aware capabilities, smart execution features, and multimodal output handling, seamlessly integrating with JupyterLab and supporting multiple notebooks simultaneously. The server is compatible with any MCP client and can work with local or hosted Jupyter deployments.

765 127 MCP

9

MCP System Monitor

Real-time system metrics for LLMs via Model Context Protocol

MCP System Monitor exposes real-time system metrics, such as CPU, memory, disk, network, host, and process information, through an interface compatible with the Model Context Protocol (MCP). The tool enables language models to retrieve detailed system data in a standardized way. It supports querying various hardware and OS statistics via structured tools and parameters. Designed with LLM integration in mind, it facilitates context-aware system monitoring for AI-driven applications.

73 17 MCP

10

LLDB-MCP

AI-assisted debugging with LLDB via Model Context Protocol integration

LLDB-MCP enables integration of the LLDB debugger with Claude's Model Context Protocol, allowing for direct control and interaction with LLDB debugging sessions through AI. The tool offers a suite of commands for managing sessions, examining program state, and controlling execution. It facilitates natural language interaction with LLDB, streamlining tasks such as loading executables, setting breakpoints, and analyzing stack traces. Designed for seamless AI-assisted debugging workflows, LLDB-MCP enhances productivity by bridging advanced debugging capabilities with AI-driven interfaces.

63 7 MCP

11

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

12

HarmonyOS MCP Server

Enables HarmonyOS device manipulation via the Model Context Protocol.

HarmonyOS MCP Server provides an MCP-compatible server that allows programmatic control of HarmonyOS devices. It integrates with tools and frameworks such as OpenAI's openai-agents SDK and LangGraph to facilitate LLM-powered automation workflows. The server supports execution through standard interfaces and can be used with agent platforms to process natural language instructions for device actions. Its design allows for seamless interaction with HarmonyOS systems using the Model Context Protocol.

25 8 MCP

13

Utopia MCP Server

A simulation MCP Server for orchestrating smart home and lifestyle devices via AI Agents.

Utopia MCP Server simulates a wide range of smart home and lifestyle devices, enabling AI agents to interact with and control them through the Model Context Protocol. It provides endpoints for managing devices like lighting, climate control, audio, security, and household robots, allowing for complex, user-centered tasks to be automated without explicit workflow programming. The server is designed to facilitate the testing and experimentation of AI-driven orchestration among multiple simulated endpoints in household environments.

9 4 MCP

14

@reapi/mcp-openapi

Serve multiple OpenAPI specs for LLM-powered IDE integrations via the Model Context Protocol.

@reapi/mcp-openapi is a Model Context Protocol (MCP) server that loads and serves multiple OpenAPI specifications, making APIs available to LLM-powered IDEs and development tools. It enables Large Language Models to access, interpret, and work directly with OpenAPI docs within code editors such as Cursor. The server supports dereferenced schemas, maintains an API catalog, and offers project-specific or global configuration. Sponsored by ReAPI, it bridges the gap between API specifications and AI-powered developer environments.

71 13 MCP

15

ScreenPilot

Empower LLMs with full device control through screen automation.

ScreenPilot provides an MCP server interface to enable large language models to interact with and control graphical user interfaces on a device. It offers a comprehensive toolkit for screen capture, mouse control, keyboard input, scrolling, element detection, and action sequencing. The toolkit is suitable for automation, education, and experimentation, allowing AI agents to perform complex operations on a user’s device.

50 8 MCP

16

AutoMobile

Powerful tools for mobile automation, test authoring, and device management via MCP.

AutoMobile provides a comprehensive set of tools for mobile automation, focusing on UI testing and development workflow automation. It operates as an MCP Server, enabling a robust interaction loop for model-driven actions and observations. The solution supports Android platforms with features like automated test authoring, multi-device management, and seamless CI test execution. AutoMobile also offers source mapping and deep view hierarchy analysis to enhance code rendering accuracy.

63 8 MCP

17

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

18

mcp-server-apache-airflow

A Model Context Protocol server for integrating Apache Airflow with MCP clients.

mcp-server-apache-airflow provides a Model Context Protocol (MCP) server implementation that allows standardized interaction with Apache Airflow environments. By wrapping Airflow's REST API, it enables MCP clients to manage and orchestrate workflows, DAGs, and runs in a consistent and interoperable manner. This implementation leverages the official Apache Airflow client library to ensure robust compatibility and maintainability. It streamlines the management of Airflow resources by exposing comprehensive endpoint coverage for key workflow operations.

109 28 MCP

19

MCP Internet Speed Test

Standardized internet speed and network performance testing for AI models via MCP.

MCP Internet Speed Test implements the Model Context Protocol (MCP) to enable AI models and agents to measure, analyze, and report diverse network performance metrics through a standardized interface. It supports download, upload, latency, jitter, and cache analysis, along with multi-CDN and geographic location detection. By offering an MCP-compatible server with robust testing features, it allows seamless integration with LLMs and AI tools for real-time network assessment and diagnostics.

11 7 MCP

20

MCP-Geo

Geocoding and reverse geocoding MCP server for LLMs.

MCP-Geo provides geocoding and reverse geocoding capabilities to AI models using the Model Context Protocol, powered by the GeoPY library. It offers various tools such as address lookup, reverse lookup from coordinates, distance calculations, and batch processing of locations, all accessible via standard MCP tool interfaces. Safety features like rate limiting and robust error handling ensure reliable and compliant usage of geocoding services. The server is compatible with environments like Claude Desktop and can be easily configured elsewhere.

28 4 MCP

21

investor-agent

Comprehensive Financial Analysis Server for LLMs via Model Context Protocol

investor-agent is an MCP server designed to deliver in-depth financial insights and market analysis to Large Language Models. It leverages real-time data sources and advanced analysis tools to provide information such as market movers, ticker data, options chains, historical prices, financial statements, ownership structure, and sentiment indices. The platform features robust caching and error handling for reliable and efficient data delivery while supporting extensible financial analytics with optional technical indicators and intraday data.

286 51 MCP

22

Beelzebub

AI-driven honeypot framework with advanced threat detection and context protocol support.

Beelzebub is an advanced honeypot framework that utilizes AI and large language models (LLMs) to realistically simulate system interactions, enabling the detection and analysis of sophisticated cyber attacks. The platform supports modular service definitions via YAML, integrates with observability stacks, and supports multiple protocols including MCP, which is used to detect prompt injection against LLM agents. Designed for security researchers and professionals, it enables the creation of distributed honeypot networks for collaborative global threat intelligence.

1,680 155 MCP

23

esp-mcp

Unified ESP-IDF command interface for LLMs and chatbots

esp-mcp provides a Model Context Protocol (MCP) compliant server to consolidate and simplify ESP-IDF and related project command execution. It enables large language models and chatbots to perform ESP-IDF project builds, flash firmware, and manage ESP devices through standardized protocol interactions. The project is proof-of-concept with support for basic build and flash commands and aims to expand towards comprehensive embedded device management via LLM interfaces.

114 11 MCP

24

tasty-agent

Model Context Protocol server for TastyTrade portfolio monitoring and trading.

tasty-agent is a Model Context Protocol (MCP) server designed to enable LLMs to interface with TastyTrade brokerage accounts. It provides functionality for monitoring portfolios, analyzing market positions, and executing trades through a standardized API. The tool offers automated implied volatility (IV) analysis, supports real-time market data streaming, and features robust rate limiting and error handling to ensure reliability. Authentication is handled via OAuth, and the tool is easily configurable for MCP clients.

47 17 MCP

25

JVM MCP Server

Lightweight multi-agent protocol server for JVM monitoring and diagnostics.

JVM MCP Server provides a lightweight, zero-dependency server that implements the Multi-Agent Communication Protocol for monitoring and diagnosing Java applications. It leverages native JDK tools to enable powerful AI agent interactions for gathering JVM metrics, analyzing memory and threads, and performing advanced diagnostics without relying on third-party software. The server supports both local and remote Java environments through SSH, ensuring cross-platform compatibility and secure operation.

71 15 MCP

26

Alby Bitcoin Payments MCP Server

Connect Bitcoin Lightning wallets to LLMs using Nostr Wallet Connect within the Model Context Protocol framework.

Alby Bitcoin Payments MCP Server provides a way to integrate Bitcoin Lightning wallets with large language models (LLMs) via the Model Context Protocol (MCP). This server supports Nostr Wallet Connect (NWC), LNURL, and L402, enabling secure payment and authentication flows. Leveraging the official MCP TypeScript SDK along with Alby SDKs and tools, it allows agents and clients to interact with lightning wallets using various transport protocols like SSE and HTTP Streamable. The server can be integrated with tools such as Claude, Goose, and Cline for seamless wallet operations within AI workflows.

36 9 MCP

27

MCP Ping-Pong Server by Remote Call

An experimental MCP-based Ping-Pong server leveraging FastAPI and FastMCP for remote calls.

MCP Ping-Pong Server by Remote Call demonstrates the use of the Model Context Protocol (MCP) for handling command-based interactions via FastAPI. The project provides a backend powered by FastAPI and FastMCP, enabling remote MCP calls through API endpoints and Server-Sent Events (SSE). It includes thread-safe session management, command handling integration, and an interactive UI for engaging with ping, pong, and count operations. Designed as an educational tool, it showcases practical MCP usage for API-driven workflows.

1 2 MCP

28

MCP BaoStock Server

Stock market data server with multiple APIs, powered by BaoStock.

MCP BaoStock Server offers a stock data API service based on BaoStock, providing endpoints for retrieving diverse market information. It supports queries for stock basics, K-line historical data, industry classification, dividends, financial indicators, index data, and valuation metrics. Developed in Python, it is designed for easy integration and rapid access to comprehensive Chinese stock market data. The server enables detailed analysis and research with example test cases for each supported endpoint.

56 19 MCP

29

k6-mcp-server

A Model Context Protocol server for orchestrating k6 load tests via MCP-enabled clients.

k6-mcp-server implements the Model Context Protocol, allowing users to execute and manage k6 load testing scripts through standardized MCP clients. It provides a simple API, supports custom test durations and virtual users, and offers real-time execution output. The system is configurable via environment variables and can be easily integrated into existing MCP-compatible tooling.

17 8 MCP

30

JMeter MCP Server

Execute and analyze JMeter tests via Model Context Protocol integration.

JMeter MCP Server enables execution and analysis of Apache JMeter tests through MCP-compatible clients. It provides command-line and programmatic tools for running JMeter tests in GUI and non-GUI modes, parsing and analyzing JTL result files, and generating detailed metrics and reports. Designed for integration with tools that follow the Model Context Protocol, it facilitates seamless performance testing workflows and actionable insights for test results.

47 16 MCP

31

Currents MCP Server

Connect AI agents to test results context via Currents MCP Server.

Currents MCP Server provides a standardized Model Context Protocol (MCP) server for integrating test results and debugging data into AI agents. It enables seamless communication between CI test data in Currents and AI-powered tools, such as Cursor Editor and Claude Desktop, facilitating actions like test optimization and failure diagnosis. The server exposes a suite of tools for retrieving detailed project, run, and performance metrics and is easily configurable via command-line for development and integration. Secure handling of API keys and support for local development are included.

14 6 MCP

32

QA Sphere MCP Server

Model Context Protocol server enabling LLMs to interact with QA Sphere test cases

QA Sphere MCP Server provides a Model Context Protocol (MCP) integration for QA Sphere, allowing Large Language Models to interact with, discover, and summarize test cases within the QA Sphere test management system. It enables AI-powered IDEs and MCP clients to reference and manipulate QA Sphere test case data within development workflows. The solution supports quick integration into clients like Claude, Cursor, and 5ire, facilitating seamless collaboration and context sharing for AI-assisted development.

15 6 MCP

33

MCP Server for Milvus

Bridge Milvus vector database with AI apps using Model Context Protocol (MCP).

MCP Server for Milvus enables seamless integration between the Milvus vector database and large language model (LLM) applications via the Model Context Protocol. It exposes Milvus functionality to external LLM-powered tools through both stdio and Server-Sent Events communication modes. The solution is compatible with MCP-enabled clients such as Claude Desktop and Cursor, supporting easy access to relevant vector data for enhanced AI workflows. Configuration is flexible through environment variables or command-line arguments.

196 57 MCP

34

Druid MCP Server

Comprehensive Model Context Protocol server for advanced Apache Druid management and analytics

Druid MCP Server provides a fully MCP-compliant interface for managing, analyzing, and interacting with Apache Druid clusters. Leveraging tools, resources, and AI-assisted prompts, it enables LLM clients and AI agents to perform operations such as time series analysis, statistical exploration, and data management through standardized protocols. The server is built with a feature-based architecture, offers real-time communication via multiple transports, and includes automatic discovery and registration of MCP components.

9 4 MCP

35

mcpmcp-server

Seamlessly discover, set up, and integrate MCP servers with AI clients.

mcpmcp-server enables users to discover, configure, and connect MCP servers with preferred clients, optimizing AI integration into daily workflows. It supports streamlined setup via JSON configuration, ensuring compatibility with various platforms such as Claude Desktop on macOS. The project simplifies the connection process between AI clients and remote Model Context Protocol servers. Users are directed to an associated homepage for further platform-specific guidance.

17 6 MCP

36

MCP Language Server

Bridge codebase navigation tools to AI models using MCP-enabled language servers.

MCP Language Server implements the Model Context Protocol, allowing MCP-enabled clients, such as LLMs, to interact with language servers for codebase navigation. It exposes standard language server features—like go to definition, references, rename, and diagnostics—over MCP for seamless integration with AI tooling. The server supports multiple languages by serving as a proxy to underlying language servers, including gopls, rust-analyzer, and pyright.

1,256 94 MCP

37

Postmancer

A standalone MCP server for API testing and management via AI assistants.

Postmancer is a Model Context Protocol (MCP) server designed to facilitate API testing and management through natural language interactions with AI assistants. It enables HTTP requests, organizes API endpoints into collections, and provides tools for managing environment variables, authentication, and request history. Postmancer is particularly aimed at integrating with AI platforms like Claude for seamless, automated API workflows.

28 4 MCP

38

Globalping MCP Server

Enable AI models to run network tests globally via natural language.

Globalping MCP Server implements the Model Context Protocol, enabling AI models to interface with a global network measurement platform through natural language. It allows AI clients to perform network diagnostic tests such as ping, traceroute, DNS, MTR, and HTTP from thousands of locations worldwide. The server offers AI-friendly context handling, detailed parameter descriptions, comparative analysis of network performance, and supports secure authentication using OAuth or API tokens.

33 3 MCP

39

Insforge MCP Server

A Model Context Protocol server for seamless integration with Insforge and compatible AI clients.

Insforge MCP Server implements the Model Context Protocol (MCP), enabling smooth integration with various AI tools and clients. It allows users to configure and manage connections to the Insforge platform, providing automated and manual installation methods. The server supports multiple AI clients such as Claude Code, Cursor, Windsurf, Cline, Roo Code, and Trae via standardized context management. Documentation and configuration guidelines are available for further customization and usage.

3 2 MCP

40

awslabs/mcp

Specialized MCP servers for seamless AWS integration in AI and development environments.

AWS MCP Servers is a suite of specialized servers implementing the open Model Context Protocol (MCP) to bridge large language model (LLM) applications with AWS services, tools, and data sources. It provides a standardized way for AI assistants, IDEs, and developer tools to access up-to-date AWS documentation, perform cloud operations, and automate workflows with context-aware intelligence. Featuring a broad catalog of domain-specific servers, quick installation for popular platforms, and both local and remote deployment options, it enhances cloud-native development, infrastructure management, and workflow automation for AI-driven tools. The project includes Docker, Lambda, and direct integration instructions for environments such as Amazon Q CLI, Cursor, Windsurf, Kiro, and VS Code.

6,220 829 MCP

41

Weaviate MCP Server

A server implementation for the Model Context Protocol (MCP) built on Weaviate.

Weaviate MCP Server provides a backend implementation of the Model Context Protocol, enabling interaction with Weaviate for managing, inserting, and querying context objects. The server facilitates object insertion and hybrid search retrieval, supporting context-driven workflows required for LLM orchestration and memory management. It includes tools for building and running a client application, showcasing integration with Weaviate's vector database.

157 38 MCP

42

just-mcp

A production-ready MCP server for Justfile command integration with LLMs.

just-mcp delivers an MCP (Model Context Protocol) server that enables seamless integration between AI assistants and the Just command runner. It provides functionality for AI models to discover, execute, and introspect Justfile recipes using a standardized protocol. The system emphasizes context abstraction, safer command execution compared to raw bash, and user-friendly interfaces for both agents and humans. Built-in safety and validation features further enhance reliability and security.

31 5 MCP

43

Tilt MCP Server

Programmatic Access to Tilt via Model Context Protocol for LLMs

Tilt MCP Server provides Model Context Protocol (MCP) capabilities to enable seamless integration between Tilt—an environment for managing Docker/Kubernetes workloads—and large language model (LLM) applications. It allows LLMs and AI assistants to interact programmatically with Tilt resources, retrieve logs, monitor status, trigger actions, and utilize guided workflows. The server exposes standardized resources, tools, and prompts for intelligent management and automation of Tilt-based development environments.

2 1 MCP

44

Product Hunt MCP Server

Plug-and-play MCP server for accessing Product Hunt data with LLMs and agents.

Product Hunt MCP Server acts as a bridge between Product Hunt’s API and any agent or LLM that supports the Model Context Protocol (MCP). It enables fast and standardized access to posts, collections, topics, users, comments, and votes on Product Hunt. Built on FastMCP, it ensures seamless compatibility with popular AI tooling like Claude Desktop and Cursor. Integration is straightforward, requiring only a Product Hunt API token and simple configuration.

33 8 MCP

45

cloudflare/mcp-server-cloudflare

Connect Cloudflare services to Model Context Protocol (MCP) clients for AI-powered management.

Cloudflare MCP Server enables integration between Cloudflare's suite of services and clients using the Model Context Protocol (MCP). It provides multiple specialized servers that allow AI models to access, analyze, and manage configurations, logs, analytics, and other features across Cloudflare's platform. Users can leverage natural language interfaces in compatible MCP clients to read data, gain insights, and perform automated actions on their Cloudflare accounts. This project aims to streamline the orchestration of security, development, monitoring, and infrastructure tasks through standardized MCP connections.

2,919 251 MCP

46

mcp

Universal remote MCP server connecting AI clients to productivity tools.

WayStation MCP acts as a remote Model Context Protocol (MCP) server, enabling seamless integration between AI clients like Claude or Cursor and a wide range of productivity applications, such as Notion, Monday, Airtable, Jira, and more. It supports multiple secure connection transports and offers both general and user-specific preauthenticated endpoints. The platform emphasizes ease of integration, OAuth2-based authentication, and broad app compatibility. Users can manage their integrations through a user dashboard, simplifying complex workflow automations for AI-powered productivity.

27 6 MCP

47

Flowcore Platform MCP Server

A standardized MCP server for managing and interacting with Flowcore Platform resources.

Flowcore Platform MCP Server provides an implementation of the Model Context Protocol (MCP) for seamless interaction and management of Flowcore resources. It enables AI assistants to query and control the Flowcore Platform using a structured API, allowing for enhanced context handling and data access. The server supports easy deployment with npx, npm, or Bun and requires user authentication using Flowcore credentials.

9 5 MCP

48

Flipt MCP Server

MCP server for Flipt, enabling AI assistants to manage and evaluate feature flags.

Flipt MCP Server is an implementation of the Model Context Protocol (MCP) that provides AI assistants with the ability to interact with Flipt feature flags. It enables listing, creating, updating, and deleting various flag-related entities, as well as flag evaluation and management. The server supports multiple transports, is configurable via environment variables, and can be deployed via npm or Docker. Designed for seamless integration with MCP-compatible AI clients.

2 7 MCP

49

books-mcp-server

A server implementation supporting Model Context Protocol integration with cherry-studio.

books-mcp-server allows users to set up a Model Context Protocol (MCP) compliant server for managing and interacting with AI models. It enables integration with cherry-studio through STDIO commands and structured server configurations. The tool provides straightforward setup instructions and supports launching the server with customizable parameters, making it suitable for various AI context management tasks.

5 2 MCP

50

mcp-server-templates

Deploy Model Context Protocol servers instantly with zero configuration.

MCP Server Templates enables rapid, zero-configuration deployment of production-ready Model Context Protocol (MCP) servers using Docker containers and a comprehensive CLI tool. It provides a library of ready-made templates for common integrations—including filesystems, GitHub, GitLab, and Zendesk—and features intelligent caching, smart tool discovery, and flexible configuration options via JSON, YAML, environment variables, or CLI. Perfect for AI developers, data scientists, and DevOps teams, it streamlines the process of setting up and managing MCP servers and has evolved into the MCP Platform for enhanced capabilities.

5 1 MCP

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