k6-mcp-server alternatives

1

locust-mcp-server

Run Locust load tests via Model Context Protocol integration.

locust-mcp-server provides a Model Context Protocol (MCP) server for executing Locust load tests, allowing seamless connection between Locust and AI-powered development environments. It offers easy configuration, real-time test output, and both headless and UI testing modes. The project features a simple API for customizable load testing scenarios and supports various runtime and user parameters.

9 6 MCP

2

metoro-mcp-server

Bridge Kubernetes observability data to LLMs via the Model Context Protocol.

Metoro MCP Server is an implementation of the Model Context Protocol (MCP) that enables seamless integration between Kubernetes observability data and large language models. It connects Metoro’s eBPF-based telemetry APIs to LLM applications such as the Claude Desktop App, allowing AI systems to query and analyze Kubernetes clusters. This solution supports both authenticated and demo modes for accessing real-time cluster insights.

45 12 MCP

3

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

4

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

5

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

6

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

7

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

8

Cross-LLM MCP Server

Unified MCP server for accessing and combining multiple LLM APIs.

Cross-LLM MCP Server is a Model Context Protocol (MCP) server enabling seamless access to a range of Large Language Model APIs including ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, and Mistral. It provides a unified interface for invoking different LLMs from any MCP-compatible client, allowing users to call and aggregate responses across providers. The server implements eight specialized tools for interacting with these LLMs, each offering configurable options like model selection, temperature, and token limits. Output includes model context details as well as token usage statistics for each response.

9 5 MCP

9

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

10

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

11

Memory MCP

A Model Context Protocol server for managing LLM conversation memories with intelligent context window caching.

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 6 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

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

14

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

15

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

16

@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

17

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

18

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

19

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

20

MCP-Typescribe

An MCP server for serving TypeScript API context to language models.

MCP-Typescribe is an open-source implementation of the Model Context Protocol (MCP) focused on providing LLMs with contextual, real-time access to TypeScript API documentation. It parses TypeScript (and other) definitions using TypeDoc-generated JSON and serves this information via a queryable server that supports tools used by AI coding assistants. The solution enables AI agents to dynamically explore, search, and understand unknown APIs, accelerating onboarding and supporting agentic behaviors in code generation.

45 6 MCP

21

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

22

Webpage Screenshot MCP Server

Capture web page screenshots programmatically for AI agent workflows.

Webpage Screenshot MCP Server enables the capture of webpage and element screenshots using Puppeteer, providing a Model Context Protocol-compliant interface for AI agents. It supports various image formats, flexible authentication, and session persistence to facilitate integration with tools like Claude and Cursor. The server simplifies visual verification and monitoring of web applications by returning base64-encoded screenshots through customizable options. It also allows manual login workflows and multi-step web interactions with session continuity.

44 4 MCP

23

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

24

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

25

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

26

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

27

MKP

A Model Context Protocol server enabling LLM-powered applications to interact with Kubernetes clusters.

MKP is a Model Context Protocol (MCP) server designed for Kubernetes environments, allowing large language model (LLM) powered applications to list, retrieve, and apply Kubernetes resources through a standardized protocol interface. Built natively in Go, it utilizes Kubernetes API machinery to provide direct, type-safe, and reliable operations without dependencies on CLI tools. MKP offers a minimalist, pluggable design enabling universal resource support, including custom resource definitions, and includes built-in rate limiting for production readiness.

54 5 MCP

28

Kubectl MCP Server

Natural language Kubernetes management for AI assistants using the Model Context Protocol.

Kubectl MCP Server enables AI assistants such as Claude and Cursor to interact with Kubernetes clusters using natural language through the Model Context Protocol (MCP). It supports a wide range of Kubernetes operations including resource management, Helm integration, monitoring, diagnostics, and advanced security features. The server is designed to handle context-aware commands, maintain session memory, and provide intelligent command construction and explanations. Integration with multiple AI assistants and flexible transport protocols are supported for a seamless user experience.

734 137 MCP

29

Bruno MCP Server

An MCP server to run Bruno API test collections via a standardized interface.

Bruno MCP Server provides an MCP-compliant server that enables running Bruno API collections and test suites, delivering detailed results through a standardized protocol interface. It supports the execution of API tests using Bruno CLI, allows integration with environment files and variables, and returns comprehensive success, summary, failure, and timing information. Designed for integration with LLMs such as Claude, it allows automated AI agents to initiate and analyze API collection runs. Deployment is streamlined through tools like Smithery and straightforward configuration for desktop use.

32 8 MCP

30

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

31

LINE Bot MCP Server

MCP server connecting LINE Messaging API with AI agents

Provides a Model Context Protocol (MCP) server implementation for integrating AI agents with the LINE Messaging API. Enables sending text and flex messages, accessing user profiles, and managing features like rich menus via MCP-compatible endpoints. Designed for connecting AI-driven context management with LINE Official Accounts for experimental and production scenarios.

493 86 MCP

32

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

33

MCP Rubber Duck

A bridge server for querying multiple OpenAI-compatible LLMs through the Model Context Protocol.

MCP Rubber Duck acts as an MCP (Model Context Protocol) server that enables users to query and manage multiple OpenAI-compatible large language models from a unified API. It supports parallel querying of various providers, context management across sessions, failover between providers, and response caching. This tool is designed for debugging and experimentation by allowing users to receive diverse AI-driven perspectives from different model endpoints.

56 7 MCP

34

Kestra Python MCP Server

Run Kestra orchestrations with Model Context Protocol support via Docker.

Kestra Python MCP Server enables integration of the Kestra workflow orchestration platform with environments that use the Model Context Protocol (MCP). It provides a Dockerized MCP server for managing workflows, executions, files, namespaces, and more, with support for both Open-Source (OSS) and Enterprise Edition (EE) setups. The server can be configured easily using environment variables and is compatible with tools like VS Code and Claude, making model-driven orchestration accessible and modular.

16 3 MCP

35

Opik MCP Server

A unified Model Context Protocol server for Opik with multi-transport IDE integration.

Opik MCP Server is an open-source implementation of the Model Context Protocol (MCP) designed for the Opik platform. It enables seamless integration with compatible IDEs and provides a unified interface to manage Opik's features such as prompts, projects, traces, and metrics. Supporting multiple transport mechanisms like stdio and experimental SSE, it simplifies workflow integration and platform management for LLM applications. The tool aims to streamline development and monitoring by offering standardized access and control over Opik's capabilities.

182 27 MCP

36

Octomind MCP Server

Let agents create and manage automated end-to-end tests with Octomind.

Octomind MCP Server enables agents to interact with Octomind's end-to-end testing platform, allowing the creation, execution, and management of automated tests. It provides a server that integrates with various clients and allows for configuration, session management, and access to multiple testing tools and resources. The server supports extensible session storage options and integrates with external platforms like TestRail and Redis for advanced scenarios. It is designed to facilitate local development and enhance test management workflows.

21 9 MCP

37

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

38

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

39

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

40

mcp-k8s

A Kubernetes MCP server enabling resource management via Model Control Protocol.

mcp-k8s is a Kubernetes server that implements the Model Control Protocol (MCP), allowing users to interact with Kubernetes clusters through MCP-compatible tools. It supports querying and managing all Kubernetes resources, including custom resources, with fine-grained control over read and write operations. The server utilizes stdio communication and integrates with both Kubernetes and Helm, facilitating resource and Helm release management. It is designed to support natural language interactions with large language models for managing, diagnosing, and learning Kubernetes operations.

129 25 MCP

41

Kubernetes MCP Server

A standardized MCP interface for Kubernetes cluster management and introspection.

Kubernetes MCP Server enables interaction with Kubernetes clusters via the Model Context Protocol, providing a consistent and standardized interface across various tools and environments. It features resource discovery, detailed inspection, metrics retrieval, resource manipulation, and supports multiple communication modes such as stdio, SSE, and streamable HTTP. Designed for flexibility and security, it allows both read-only and full-access modes and works with different Kubernetes contexts.

117 21 MCP

42

Aiven MCP Server

Model Context Protocol server enabling LLMs to access and manage Aiven cloud data services.

Aiven MCP Server implements the Model Context Protocol (MCP) to provide secure access to Aiven's PostgreSQL, Kafka, ClickHouse, Valkey, and OpenSearch services. It enables Large Language Models (LLMs) to seamlessly integrate and interact with these cloud data platforms, supporting full stack solution development. The server offers streamlined tools for project and service management via standardized APIs and supports integration with platforms like Claude Desktop and Cursor. Environment variable configuration and explicit permission controls are used to ensure secure and flexible operations.

11 11 MCP

43

McGravity

Unified load balancer and proxy for multiple MCP servers

McGravity acts as a scalable unified proxy and load balancer for multiple MCP (Model Context Protocol) servers. It allows clients to connect through a single endpoint to access and manage multiple MCP servers efficiently. The tool offers load balancing, configuration via YAML, CLI and Docker support, and plans to evolve with features such as health checks and a web interface. Designed for modern GenAI infrastructure, it simplifies connection, balancing, and scalability of MCP server deployments.

68 3 MCP

44

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

45

Quarkus Model Context Protocol Servers

Extensible Java-based servers implementing the Model Context Protocol for context-aware LLM integrations.

Quarkus Model Context Protocol Servers offers a collection of Java-based servers implementing the Model Context Protocol (MCP) to extend the capabilities of language model applications. Built with the Quarkus MCP server framework, it enables integration with JDBC databases, JVM processes, file systems, JavaFX, Kubernetes, containers, and Wolfram Alpha. The project allows easy deployment and extension of context-aware services for AI applications via MCP. Its servers can be run across different environments using jbang and are easily extensible for new capabilities.

176 46 MCP

46

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

47

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

48

mcp-k8s-eye

Kubernetes management and diagnostics tool with MCP protocol support.

mcp-k8s-eye enables users to manage and analyze Kubernetes clusters using standardized Model Context Protocol (MCP) interfaces. It offers comprehensive resource operations, diagnostics, and resource usage monitoring through both stdio and SSE transports. Supporting generic and custom resource management along with advanced diagnostic tooling, it is geared for integration with AI clients and other MCP consumers.

26 9 MCP

49

VictoriaLogs MCP Server

MCP server enabling advanced read-only access and observability for VictoriaLogs

VictoriaLogs MCP Server implements the Model Context Protocol (MCP) to provide seamless, read-only integration with VictoriaLogs instances. It enables comprehensive access to VictoriaLogs APIs, allowing for log querying, exploration, and advanced observability tasks. The server includes embedded and searchable documentation and supports automation and interaction capabilities via standardized MCP tools. Designed to combine with other MCP servers, it enhances engineering workflows for log analysis and troubleshooting.

30 7 MCP

50

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

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