Example Clients ↗
noOriginal Documentation
Documentation Index#
Fetch the complete documentation index at: https://modelcontextprotocol.io/llms.txt Use this file to discover all available pages before exploring further.
A list of applications that support MCP integrations
export const FEATURES = [“Resources”, “Prompts”, “Tools”, “Discovery”, “Instructions”, “Sampling”, “Roots”, “Elicitation”, “CIMD”, “DCR”, “Tasks”, “Apps”];
export const FEATURE_COLORS = { Resources: “blue”, Prompts: “blue”, Tools: “blue”, Instructions: “purple”, Discovery: “purple”, Sampling: “green”, Roots: “green”, Elicitation: “green”, Tasks: “orange”, Apps: “orange”, DCR: “yellow”, CIMD: “yellow” };
export const FeatureBadge = ({feature}) => {
const color = FEATURE_COLORS[feature.split(" (")[0]] || “gray”;
return
export const filterStore = { state: { selectedFeatures: [], searchText: “”, visibleCount: 0, totalCount: 0 }, listeners: new Set(), setState(updater) { if (typeof updater === “function”) { this.state = { …this.state, …updater(this.state) }; } else { this.state = { …this.state, …updater }; } this.listeners.forEach(fn => fn(this.state)); }, subscribe(fn) { this.listeners.add(fn); return () => this.listeners.delete(fn); } };
export const useFilterStore = () => { const [state, setState] = useState(filterStore.state); useEffect(() => filterStore.subscribe(setState), []); return state; };
export const useFilter = ({name, supports}) => { const {selectedFeatures, searchText} = useFilterStore(); const isVisible = name.toLowerCase().includes(searchText.toLowerCase()) && selectedFeatures.every(feature => supports?.includes(feature)); useEffect(() => { filterStore.setState(s => ({ totalCount: s.totalCount + 1 })); return () => filterStore.setState(s => ({ totalCount: s.totalCount - 1 })); }, []); useEffect(() => { if (isVisible) { filterStore.setState(s => ({ visibleCount: s.visibleCount + 1 })); return () => filterStore.setState(s => ({ visibleCount: s.visibleCount - 1 })); } }, [isVisible]); return isVisible; };
export const ClientFilter = () => { const {selectedFeatures, searchText, visibleCount, totalCount} = useFilterStore(); useEffect(() => { filterStore.setState({ selectedFeatures: [], searchText: "" }); }, []); const toggleFeature = feature => { const newFeatures = selectedFeatures.includes(feature) ? selectedFeatures.filter(f => f !== feature) : […selectedFeatures, feature]; filterStore.setState({ selectedFeatures: newFeatures }); }; const clearFilters = () => { filterStore.setState({ selectedFeatures: [], searchText: "" }); }; const hasFilters = selectedFeatures.length > 0 || searchText.length > 0; return
<span className="font-bold text-gray-700 dark:text-gray-300">
Showing {visibleCount} of {totalCount} clients
</span>
{hasFilters && <button onClick={clearFilters} className="text-sm cursor-pointer px-3 py-1 rounded-full bg-gray-200 dark:bg-gray-700 text-gray-700 dark:text-gray-300 hover:bg-gray-300 dark:hover:bg-gray-600 transition-colors">
<Icon icon="xmark" iconType="solid" size={16} /> Clear filters
</button>}
<input type="text" placeholder="Search clients by name..." value={searchText} onChange={e => filterStore.setState({
searchText: e.target.value })} className=“w-full px-3 py-2 border border-gray-300 dark:border-gray-600 rounded-lg bg-white dark:bg-gray-800 text-gray-900 dark:text-gray-100 placeholder-gray-500 dark:placeholder-gray-400 focus:outline-none focus:ring-2 focus:ring-primary focus:border-transparent” />
Filter by features:
{FEATURES.map(feature => <button key={feature} onClick={() => toggleFeature(feature)}`}>
<Icon icon={selectedFeatures.includes(feature) ? "square-check" : "square"} iconType={selectedFeatures.includes(feature) ? "solid" : "regular"} size={16} />
{feature}
</button>)}
; };
export const McpClient = ({name, homepage, supports, sourceCode, instructions, children}) => { const slug = name.toLowerCase().replace(/[().\s-]+/g, “-”).replace(/^-|-$/g, “”); if (homepage?.match(/^https://github.com/[^/]+/[^/]+/)) { sourceCode ??= homepage; } const features = (supports ?? “”).split(", “).sort((a, b) => { const featureA = a.split(” (")[0]; const featureB = b.split(" (")[0]; return FEATURES.indexOf(featureA) - FEATURES.indexOf(featureB); }); const instructionsLinks = Array.isArray(instructions) ? <> Configuration instructions: {instructions.map(([text, url], i) => [i > 0 && “, “, {text}])} </> : Configuration instructions ; const [expanded, setExpanded] = useState(false); const [hasOverflow, setHasOverflow] = useState(false); const contentRef = useRef(null); const isVisible = useFilter({ name, supports }); useEffect(() => { const el = contentRef.current; if (el) { setHasOverflow(el.scrollHeight > el.clientHeight); } }, []); if (!isVisible) return null; return
<a href={homepage} className="border-0 text-xl font-semibold text-primary underline" target="_blank" rel="noopener noreferrer">
{name}
</a>
<a href={`#${slug}`} className="ml-auto border-0 opacity-0 group-hover:opacity-100 text-xl text-gray-400 hover:text-gray-600" aria-label={`Link to ${name}`}>
#
</a>
{features.length > 0 &&
<span className="inline-flex items-center h-[1lh]">
{'\u200B'}<Icon icon="check" iconType="solid" size={18} />
</span>
<strong>Supports:</strong>
<span className="flex flex-wrap gap-1">
{features.map(feature => <FeatureBadge key={feature} feature={feature} />)}
</span>
}
{sourceCode &&
<span className="inline-flex items-center h-[1lh]">
{'\u200B'}<Icon icon="code" iconType="solid" size={18} />
</span>
<span>
<a href={sourceCode} target="_blank" rel="noopener noreferrer">
Open source
</a>
</span>
}
{instructions &&
<span className="inline-flex items-center h-[1lh]">
{'\u200B'}<Icon icon="gear" iconType="solid" size={18} />
</span>
<span>
{instructionsLinks}
</span>
}
{children}
{hasOverflow && <button onClick={() => setExpanded(!expanded)}`}>
<span bg-white/60 dark:bg-gray-900/60 rounded-full`}>
<Icon icon="chevron-down" iconType="solid" size={18} />
</span>
</button>}
; };
This page showcases applications that support the Model Context Protocol (MCP). Each client may support different MCP features:
| Feature | Description |
|---|---|
| Server-exposed data and content | |
| Pre-defined templates for LLM interactions | |
| Executable functions that LLMs can invoke | |
| Support for tools/prompts/resources changed notifications | |
| Server-provided guidance for LLMs | |
| Server-initiated LLM completions | |
| Filesystem boundary definitions | |
| User information requests | |
| Client ID Metadata Document support | |
| Dynamic Client Registration support | |
| Long-running operation tracking | |
| Interactive HTML interfaces |
This list is maintained by the community. If you notice any inaccuracies or would like to add or update information about MCP support in your application, please submit a pull request.
Client details#
Key features:
- Built-in MCP servers can be quickly enabled and disabled.
- Users can add more servers by modifying the configuration file.
- It is open-source and user-friendly, suitable for beginners.
- Future support for MCP will be continuously improved.
Key features:
- Multi-LLM – We support most LLM APIs (OpenAI, Anthropic, Gemini, Ollama, and all OpenAI API Compatible).
- Built-in support for MCP Servers.
- Create agentic flows in a type- and memory-safe language like Rust.
Learn more:
Key features:
- No-code AI agent creation and workflow building.
- Access a vast library of 10,000+ tools and 2,500+ APIs through MCP.
- Simple 3-step process to connect MCP servers.
- Securely manage connections and revoke access anytime.
Learn more:
Key features:
- Dynamic LLM API & Agent Switching: Seamlessly toggle between different LLM APIs and agents on the fly.
- Comprehensive Capabilities Support: Built-in support for tools, prompts, resources, and sampling methods.
- Configurable Agents: Enhanced flexibility with selectable and customizable tools via agent settings.
- Advanced Sampling Control: Modify sampling parameters and leverage multi-round sampling for optimal results.
- Cross-Platform Compatibility: Fully compatible with macOS, Windows, and Linux.
- Free & Open-Source (FOSS): Permissive licensing allows modifications and custom app bundling.
Learn more:
Key features:
- Full support for MCP servers.
- Edit prompts using your preferred text editor.
- Access saved prompts instantly with
@. - Control and organize AWS resources directly from your terminal.
- Tools, profiles, context management, auto-compact, and so much more!
Get Started
brew install amazon-qKey features:
- Support for the VSCode, JetBrains, Visual Studio, and Eclipse IDEs.
- Control and organize AWS resources directly from your IDE.
- Manage permissions for each MCP tool via the IDE user interface.
Key features:
- Granular control over enabled tools and permissions
- Support for MCP servers defined in VS Code
mcp.json
Key features:
- Full Feature Support: Debug Tools, Prompts, and Resources of MCP servers with a user-friendly GUI.
- Dual Transport Modes: Supports both STDIO for local processes and HTTP for remote servers.
- Easy Setup: Automatically parses MCP configuration files and supports direct command or URL input.
- Authentication: Supports OAuth 2.0, API Key, Bearer Token, and other methods for secure connections.
Key features:
- Connects to any MCP server via SSE.
- Works with the Apify MCP Server to interact with one or more Apify Actors.
- Dynamically utilizes tools based on context and user queries (if supported by the server).
Key features:
- Multi-LLM Compatibility: Works seamlessly with all leading AI platforms including Claude, OpenAI (ChatGPT), Gemini, xAI, and OpenRouter. Deploy the same agent across different platforms without modification.
- Optimized for Cost & Performance: Dynamic tool loading loads tools only when needed, enabling thousands of tools without context bloat. Tool output optimization provides up to 99% payload reduction via compact JSON representation. Parallel execution runs multiple tool calls simultaneously for 10x faster responses.
- Unified Multi-Tool Interface: Mesh multiple APIs and MCP servers into a single agent. Interact with all tools seamlessly from one Copilot interface without glue code or framework-specific logic.
- Governed Access & Audit: Fine-grained access control defines exactly which operations each user or agent can perform. Complete audit trail tracks every tool call with timestamps, inputs, and outputs for compliance.
Learn more:
Key features:
- Full MCP support in local and remote agents.
- Add additional context through MCP servers.
- Automate your development workflows with MCP tools.
- Works in VS Code and JetBrains IDEs.
Key features:
- MCP tools and resources can be used
- Supports avatar-to-avatar communication via socket.io.
- Supports the mixed use of multiple LLM APIs.
- The daemon mechanism allows for flexible scheduling.
Key features:
- Seamlessly incorporate MCP tools into agentic workflows.
- Quickly instantiate framework-native tools from connected MCP client(s).
- Planned future support for agentic MCP capabilities.
Learn more:
Key features:
- MCP Tool integrations: once configured, user can enable individual MCP server in each chat
- MCP quick setup: import configuration from Claude Desktop app or Cursor editor
- Invoke MCP tools inside any app with AI Command feature
- Integrate with remote MCP servers in the mobile app
Learn more:
Key features:
- Save transcriptions from Zoom, Google Meet, and more
- MCP Tools for voice AI agents
- Remote MCP servers support
Key features:
- Tools support for MCP servers
- Support both local and remote MCP servers
- Built-in MCP servers marketplace
Key features:
- Unified access to top LLM providers (OpenAI, Anthropic, DeepSeek, xAI, and more) in one interface
- Built-in retrieval-augmented generation (RAG) for instant, private search across your PDFs, text, and code files
- Plug-in system for custom tools via Model Context Protocol (MCP) servers
- Multimodal chat: supports images, text, and live interactive artifacts
Key features:
- Support for MCP via connections UI in settings
- Access to search tools from configured MCP servers for deep research
- Support for MCP Apps, allowing ChatGPT to connect to MCP-based applications
- Enterprise-grade security and compliance features
Key features:
- Tools support for MCP servers
- Offer built-in tools like web search, artifacts and image generation.
Key features:
- MCP support with one-click install
- Built in tools, like web search, terminal, and image generation
- Chat with multiple models at once (cloud or local)
- Create projects with scoped memory
- Quick chat with an AI that can see your screen
Key features:
- Full support for resources, prompts, tools, and roots from MCP servers
- Offers its own tools through an MCP server for integrating with other MCP clients
<McpClient name=“Claude Desktop App” homepage=“https://claude.ai/download" supports=“Resources, Prompts, Tools, Apps, DCR” instructions={[ [“Local servers”, “https://support.claude.com/en/articles/10949351-getting-started-with-local-mcp-servers-on-claude-desktop”], [“Remote servers”, “https://support.claude.com/en/articles/11175166-getting-started-with-custom-connectors-using-remote-mcp”] ]}
Claude Desktop provides comprehensive support for MCP, enabling deep integration with local tools and data sources.
Key features:
- Full support for resources, allowing attachment of local files and data
- Support for prompt templates
- Tool integration for executing commands and scripts
- Local server connections for enhanced privacy and security
Key features:
- Support for remote MCP servers via integrations UI in settings
- Access to tools, prompts, and resources from configured MCP servers
- Seamless integration with Claude’s conversational interface
- Enterprise-grade security and compliance features
Key features:
- Create and add tools through natural language (e.g. “add a tool that searches the web”)
- Share custom MCP servers Cline creates with others via the
~/Documents/Cline/MCPdirectory - Displays configured MCP servers along with their tools, resources, and any error logs
Key features:
- Use MCP tools from any configured MCP server
- Seamless integration with VS Code and Jetbrains UI
- Supports multiple LLM providers and custom endpoints
Learn more:
Key features:
- Support for MCP tools (listing and invocation)
- Support for MCP resources (list, read, and templates)
- Elicitation support (routes requests to TUI for user input)
- Supports STDIO and HTTP streaming transports with OAuth
- Also available as VS Code extension
Key features:
- Type “@” to mention MCP resources
- Prompt templates surface as slash commands
- Use both built-in and MCP tools directly in chat
- Supports VS Code and JetBrains IDEs, with any LLM
Key features:
- Support for MCP tools and resources
- Integration with development workflows
- Extensible AI capabilities
Key features:
- Support for MCP tools in Cursor Composer
- Support for roots
- Support for prompts
- Support for elicitation
- Support for both STDIO and SSE
Key features:
- Supports MCP Servers in config
- Exposes MCP Client
Key features:
- Editor-agnostic: protocol for any editor to integrate.
- Single configuration: Configure eca making it work the same in any editor via global or local configs.
- Chat interface: ask questions, review code, work together to code.
- Agentic: let LLM work as an agent with its native tools and MCPs you can configure.
- Context: support: giving more details about your code to the LLM, including MCP resources and prompts.
- Multi models: Login to OpenAI, Anthropic, Copilot, Ollama local models and many more.
- OpenTelemetry: Export metrics of tools, prompts, server usage.
Key features:
- Provides MCP tool support for Emacs.
Key features:
- PDF and Image support, based on MCP Native types
- Interactive front-end to develop and diagnose Agent applications, including passthrough and playback simulators
- Built in support for “Building Effective Agents” workflows.
- Deploy Agents as MCP Servers
Key features:
- Tool integration for executing commands and scripts via STDIO, SSE indirectly supported via mcp-remote npm package.
- Local server connections for enhanced privacy and security
- MCPs can be installed via project rules or local workstation rules files.
- Individual tools within MCPs can be turned off.
Key features:
- Seamless MCP Integration: Easily connect to MCP servers to utilize a wide range of external tools.
- Privacy-First Design: Your data stays on your device. We don’t collect any user data, ensuring complete privacy.
- Lightweight & Efficient: A compact and optimized design ensures a smooth and responsive experience with any AI model.
- Broad Compatibility: Works with all OpenAI-compatible service providers and supports local offline models through MLX.
- Rich User Experience: Features beautifully formatted Markdown, blazing-fast text rendering, and intelligent, automated chat titling.
Learn more:
Key features:
- Environment & API Key Management
- Model Management
- MCP Server Integration
- Workflow Orchestration
- Chat Interface
Key features:
- JavaScript toolbox to work with prompts
- Abstraction to make it easy and productive
- Seamless Visual Studio Code integration
Key features:
- Client support for tools and prompts (resources partially supported)
- Rich discovery with support in Genkit’s Dev UI playground
- Seamless interoperability with Genkit’s existing tools and prompts
- Works across a wide variety of GenAI models from top providers
Key features:
- Delegate tasks to Copilot from GitHub Issues, Visual Studio Code, GitHub Copilot Chat or from your favorite MCP host using the GitHub MCP Server
- Tailor Copilot to your project by customizing the agent’s development environment or writing custom instructions
- Augment Copilot’s context and capabilities with MCP tools, with support for both local and remote MCP servers
Key features:
- Integrated MCP Server Directory
- Integrated MCP Tool Directory
- Host MCP servers and access them via the Chat or SSE endpoints – Ability to chat with multiple LLMs and MCP servers at once
- Upload and analyze local files and data
- Full-text search across all your chats and data
Key features:
- Expose MCP functionality to goose through tools.
- MCPs can be installed directly via the extensions directory, CLI, or UI.
- goose allows you to extend its functionality by building your own MCP servers.
- Includes built-in extensions for development, memory, computer control, and auto-visualization.
Key features:
- CLI-first design with a focus on simplicity and ease of use
- Rich set of built-in tools for shell commands, Python execution, file operations, and web browsing
- Local-first approach with support for multiple LLM providers
- Open-source, built to be extensible and easy to modify
Key features:
- AI Commands: Simple APIs like page.ai(), page.extract() and executeTask() for any AI automation
- Fallback to Regular Playwright: Use regular Playwright when AI isn’t needed
- Stealth Mode – Avoid detection with built-in anti-bot patches
- Cloud Ready – Instantly scale to hundreds of sessions via Hyperbrowser
- MCP Client – Connect to tools like Composio for full workflows (e.g. writing web data to Google Sheets)
Key features:
- Design Mode: Move elements, edit text, and zoom in to interact with your front-end like Figma.
- Agent Connect: Plug in Cursor, Claude Code, or Codex.
- Version Control: Stage changes and open PRs from Inspector.
- MCP Client: Connect any MCP Server you want!
Key features:
- 30+ pre-integrated MCP servers with one-click integration of custom servers
- MCP recommendation capability that suggests the best servers for specific tasks
- Multi-agent architecture with leading tool use reliability and scalability, supporting unlimited concurrent MCP server connections through RAG-powered server metadata
- Model agnostic platform supporting any leading LLMs (OpenAI, Anthropic, Google, etc.)
- Unlimited chat history and global persistent memory powered by RAG
- Easy creation of custom agents with custom models, instructions, knowledge bases, and MCP servers
- Local MCP server (STDIO) support coming soon with desktop apps
Key features:
- Unlimited code completion powered by Mellum, JetBrains’ proprietary AI model.
- Context-aware AI chat that understands your code and helps you in real time.
- Access to top-tier models from OpenAI, Anthropic, and Google.
- Offline mode with connected local LLMs via Ollama or LM Studio.
- Deep integration into IDE workflows, including code suggestions in the editor, VCS assistance, runtime error explanation, and more.
Key features:
- Connects to MCP servers over stdio to use external tools and data sources.
- Per-command approval with an optional allowlist.
- Config via
mcp.json(global~/.junie/mcp.jsonor project.junie/mcp/).
Key features:
- Create and add tools through natural language (e.g. “add a tool that searches the web”)
- Discover MCP servers via the MCP Marketplace
- One click MCP server installs via MCP Marketplace
- Displays configured MCP servers along with their tools, resources, and any error logs
Key features:
- Slack/Discord/Web MCP clients for using MCPs directly
- Simple web UI dashboard for easy MCP configuration
- Direct OAuth integration with Slack & Discord Clients and MCP Servers for secure user authentication
- SSE transport support
Learn more:
Key features:
- Remote MCP Server (SSE & Streamable HTTP) support, connect to any MCP server via OAuth, API Key, or without authentication.
- MCP Tool discovery and management, including tool confirmation UI.
- Enterprise-grade security and compliance features
Key features:
- Full support for using MCP server tools to build agents and flows.
- Export agents and flows as MCP server
- Local & remote server connections for enhanced privacy and security
Learn more:
Key features:
- Extend current tool ecosystem, including Code Interpreter and Image generation tools, through MCP servers
- Add tools to customizable Agents, using a variety of LLMs from top providers
- Open-source and self-hostable, with secure multi-user support
- Future roadmap includes expanded MCP feature support
Key features:
- Use MCP servers with local models on your computer. Add entries to
mcp.jsonand save to get started. - Tool confirmation UI: when a model calls a tool, you can confirm the call in the LM Studio app.
- Cross-platform: runs on macOS, Windows, and Linux, one-click installer with no need to fiddle in the command line
- Supports GGUF (llama.cpp) or MLX models with GPU acceleration
- GUI & terminal mode: use the LM Studio app or CLI (lms) for scripting and automation
Learn more:
Key features:
- Consume MCP server tools over HTTP/JSON-RPC 2.0 (initialize, list tools, call tools).
- Programmatic tool discovery and invocation via
McpClient. - Easy integration in .NET agents and applications.
Learn more:
Key features:
- Easy MCP Integration: Connecting Lutra to MCP servers is as simple as providing the server URL; Lutra handles the rest behind the scenes.
- Chat to Take Action: Lutra understands your conversational context and goals, automatically integrating with your existing apps to perform tasks.
- Reusable Playbooks: After completing a task, save the steps as reusable, automated workflows—simplifying repeatable processes and reducing manual effort.
- Shareable Automations: Easily share your saved playbooks with teammates to standardize best practices and accelerate collaborative workflows.
Learn more:
Key features:
- Unified Control Panel: Manage all your MCP servers — both Local STDIO and Remote HTTP/SSE — from one clear macOS window. Start, stop, or edit them instantly without touching configs.
- One Click, All Connected: Launch or disable entire MCP setups with one toggle. Switch bundles per project or workspace and keep your AI tools synced automatically.
- Per-Tool Control: Enable or hide individual tools inside each server. Keep your bundles clean, lightweight, and tailored for every AI workflow.
- Instant Health & Logs: Real-time health indicators and request logs show exactly what’s running. Diagnose and fix connection issues without leaving the app.
- Auto-Generate MCP Config: Copy a ready-made JSON snippet for any client in seconds. No manual wiring — connect your Bundler as a single MCP endpoint.
Learn more:
Key features:
- Discover and inspect available tools with parameter schemas and descriptions
- Supports OAuth and API key authentication for secure provider connections
- Execute MCP tools with form-based and chat based input
- Implements Apps for rendering interactive UI responses from tools
- Streamable HTTP transport for remote MCP server connections
Key features:
- Automatic connection management of MCP servers.
- Expose tools from multiple servers to an LLM.
- Implements every pattern defined in Building Effective Agents.
- Supports workflow pause/resume signals, such as waiting for human feedback.
Key features:
- It supports standard MCP tool calling and includes both a custom MCP server and a standalone UI for testing MCP tools outside the chat flow.
- All MCP tools are provided to the LLM by default, but the project also includes an optional
@toolnamemention feature to make tool invocation more explicit—particularly useful when connecting to multiple MCP servers with many tools. - Visual workflow builder that lets you create custom tools by chaining LLM nodes and MCP tools together. Published workflows become callable as
@workflow_nametools in chat, enabling complex multi-step automation sequences.
Key features:
- Very simple interface to connect any LLM to any MCP.
- Support the creation of custom agents, workflows.
- Supports connection to multiple MCP servers simultaneously.
- Supports all langchain supported models, also locally.
- Offers efficient tool orchestration and search functionalities.
Key features:
- Swiss Army knife for MCP: supports stdio and streamable HTTP, server config files and zero config, OAuth 2.1, HTTP headers, and main MCP features.
- Persistent sessions for interaction with multiple servers simultaneously.
- Structured text output enables AI agents to explore and interact with MCP servers.
- JSON output and schema validation allow stable integration with other CLI tools, scripting, and MCP code mode in a shell.
- Proxy MCP server to provide AI code sandboxes with secure access to authenticated MCP sessions.
Key features:
- Install, configure and manage MCP servers with an intuitive UI.
- Built-in Neovim MCP server with support for file operations (read, write, search, replace), command execution, terminal integration, LSP integration, buffers, and diagnostics.
- Create Lua-based MCP servers directly in Neovim.
- Integrates with popular Neovim chat plugins Avante.nvim and CodeCompanion.nvim
Key features:
- Local emulator for ChatGPT Apps SDK and MCP ext-apps. No more ChatGPT subscription or ngrok needed.
- OAuth debugger to visually inspect MCP server OAuth at every step.
- LLM playground to chat with your MCP server against any LLM. We provide our own API tokens for free.
- Connect, test, and inspect any MCP server that’s local or remote. Manually invoke MCP tools, resource, prompts, etc. View all JSON-RPC logs.
- Supports all transports - STDIO, SSE, and Streamable HTTP.
Key features:
- Support for resources, prompts, tools, and sampling
- Agentic mode with ReAct and orchestrator capabilities
- Seamless integration with OpenAI models and other LLMs
- Dynamic tool and resource management across multiple servers
- Support for both stdio and SSE transport protocols
- Comprehensive tool orchestration and resource analysis capabilities
Key features:
- Prompt-to-MCP Server: Generate fully functional MCP servers from natural language descriptions
- Self-Testing & Debugging: Autonomously test, debug, and improve created MCP servers
- Universal MCP Client: Works with any MCP server through intuitive, natural language integration
- Curated MCP Directory: Access to tested, one-click installable MCP servers (Neon, Netlify, GitHub, Context7, and more)
- Multi-Server Orchestration: Leverage multiple MCP servers simultaneously for complex workflows
Learn more:
Key features:
- Build GraphRAG workflows powered by knowledge graphs as the data backbone
- Connect remote MCP servers via
SSEorStreamable HTTP - Support for MCP resources, prompts, tools, sampling, elicitation, and instructions
- Create multiple agents with different configurations for easy comparison and debugging
- Works with various LLM providers (OpenAI, Azure OpenAI, Anthropic, Gemini, Ollama, DeepSeek)
- Available as a Desktop app or Docker container
Learn more:
Key features:
- Support for MCP tools
- Extend Copilot Studio agents with MCP servers
- Leveraging Microsoft unified, governed, and secure API management solutions
Key features:
- Build custom AI agents with no-code
- Connect any SSE MCP server to extend agent tools
- Create multi-agent workflows for complex business processes
- User-friendly for both technical and non-technical professionals
- Ongoing development with continuous improvement of MCP support
Learn more:
Key features:
- Remote MCP server integration
- Enterprise-grade security
- Low-latency, high-throughput interactions with structured data
Learn more:
Key features:
- Web-based interface for remote MCP server connections
- Header-based Authorization support for secure server access
- OAuth authentication integration
- OpenRouter API Key support for accessing various LLM providers
- No installation required - accessible from any web browser
Key features:
- Accessible from any PC or smartphone—no installation required
- Choose your preferred LLM provider
- Supports
SSE,Streamable HTTP,npx, anduvxMCP servers - OAuth and sampling support
- New features added daily
Key features:
- Toolbox & Toolsets: Connect AI models to local tools and scripts using MCP-compliant configurations. Group tools into Toolsets to enable dynamic, multi-step workflows within conversations.
- Turnstiles: Create automated, multi-step AI interactions, allowing for complex data processing and decision-making flows.
- Real-Time Data Integration: Enhance AI responses with up-to-date information by integrating real-time web search capabilities.
- Split Chats & Branching: Engage in parallel conversations with multiple models simultaneously, enabling comparative analysis and diverse perspectives.
Learn more:
Key features:
- Instant MCP integration: Connect any remote MCP server to your collection in seconds
- Built-in RAG: Automatically get retrieval-augmented generation out of the box
- Secure OAuth: Safe, token-based authorization when connecting to servers
- Smart previews: See what each MCP server can do and selectively enable the tools you need
Learn more:
Key features:
- Acts as an MCP client to consume remote tools
- Acts as an MCP server to expose tools
- Framework agnostic and compatible with LangChain, CrewAI, Semantic Kernel, and custom agents
- Includes built-in observability and evaluation tools
Learn more:
Key features:
- Support for MCP tools
- Support for MCP resources in the cli using
@prefix - Support for MCP prompts in the cli as slash commands using
/prefix
Key features:
- Supports MCP tools in OpenSumi
- Supports built-in IDE MCP servers and custom MCP servers
Key features:
- Support for multiple fully customizable chat sessions with Ollama connected with tools.
- Support for MCP tools.
Key features:
- Full support of all major MCP features (tools, prompts, resources, and subscriptions)
- Fast, seamless UI for debugging MCP capabilities
- MCP config integration (Claude, VSCode, etc.) for fast first-time experience in testing MCPs
- Integration with history, variables, and collections for reuse and collaboration
Key features:
- AI-Powered Debugging: Ask AI to analyze captured traffic, find specific requests, or explain API responses
- Hands-Free Rule Creation: Create breakpoints, map local/remote rules through conversation
- Traffic Inspection Tools: Get flows, flow details, export cURL commands, and filter traffic with multiple criteria
- Session Control: Clear sessions, toggle recording, and manage SSL proxying domains
- Secure by Design: Localhost-only server with per-session token authentication
Learn more:
Key features:
- Local AI: Support MCP with Ollama models.
- MCP Tools: Individual MCP server management. Easily visualize the connection states of MCP servers.
- MCP Import: Import configuration from Claude Desktop app or JSON
Learn more:
Learn more:
Key features:
- Support for MCP tools and resources
- Integration with development workflows
- Extensible AI capabilities
Key features:
- Easy MCP Integration within your browser: Just open the Chrome Extension, add the server URL, and prompt server calls with the web as context!
- Remote control your browser by turning your browser into MCP Server: Just copy/paste MCP URL into any MCP Client (no npx needed), and trigger agentic browser workflows!
- Prompt our agent to execute workflows combining web agentic actions with MCP tool calls; find someone’s email on the web and then send them an email with Zapier MCP.
- Reusable and Schedulable Automations: After running a workflow, easily rerun or put on a schedule to execute in the background while you do other tasks in your browser.
Key features:
- MCP tool integration for enhanced email workflows
- Rich UI for adding, managing and interacting with a wide range of MCP servers
- Support for both remote (Streamable HTTP and SSE) and local (Stdio) MCP servers
- AI assistance for managing your emails, calendar, tasks and other third-party services
Key features:
- MCP Store: Marketplace for productivity tools and MCP server integrations
- Parallel Tasking: Run multiple AI tasks simultaneously with MCP tool support
- Model Catalogue: Access to frontier models with MCP tool integration
- Hosted MCP Servers: Plug-and-play MCP integrations with no technical setup
- Advanced MCPs: Specialized tools like Tripo3D (3D creation), Podcast Maker, and Video Maker
- Enterprise Ready: Flexible workspaces with granular access control for MCP tools
Learn more:
Key features:
- Supports Popular LLM Providers: Integrates seamlessly with leading large language model providers such as OpenAI, Anthropic, and Ollama, allowing users to leverage advanced conversational AI and orchestration capabilities within Slack.
- Dynamic and Secure Integration: Supports dynamic registration of MCP tools, works in both channels and direct messages and manages credentials securely via environment variables or Kubernetes secrets.
- Easy Deployment and Extensibility: Offers official Docker images, a Helm chart for Kubernetes, and Docker Compose for local development, making it simple to deploy, configure, and extend with additional MCP servers or tools.
Key features:
- One-click connect to MCP servers via URL or from Smithery’s registry
- Develop MCP servers that are running on localhost
- Inspect tools, prompts, resources, and sampling configurations with live previews
- Run conversational or raw tool calls to verify MCP behavior before shipping
- Full OAuth MCP-spec support
Key features:
- Built-in MCP compatibility for AI agents
- Open-source TypeScript framework
- Observable agent architecture
- Native support for MCP tools integration
Key features:
- Use tools from MCP servers in assistants embedded via React components or script tags
- SSE transport support
- Use any AI model from any AI provider (OpenAI, Anthropic, Ollama, others)
Key features:
- Native Google Sheets add-on providing effortless access to MCP capabilities
- Supports OAuth 2.1 and header-based authentication for secure and flexible connections
- Compatible with both SSE and Streamable HTTP transport for efficient, real-time streaming communication
- Fully web-based, cross-platform client requiring no additional software installation
Key features:
- Connects to MCP servers via SSE transport for real-time tool integration
- Automatic tool discovery and loading from MCP servers
- Support for distributed tool functionality across multiple agents
- Enterprise-ready with high availability and observability features
- Modular architecture supporting multiple AI model providers
Learn more:
Key features:
- Native Mobile Experience: Access and manage your MCP servers anytime, anywhere on both Android and iOS devices
- Advanced AI-Powered Voice Recognition: Sophisticated voice recognition engine enhanced with cutting-edge AI and Natural Language Processing (NLP), specifically tuned to understand complex developer terminology and command structures
- Unified Multi-MCP Server Management: Effortlessly manage and interact with multiple Model Context Protocol (MCP) servers from a single, centralized mobile application
Key features:
- Hosted platform with React SDK for integrating chat or other LLM-based experiences into your own app.
- Support for selection of arbitrary React components in the chat experience, with state management and tool calling.
- Support for MCP servers, from Tambo’s servers or directly from the browser.
- Supports OAuth 2.1 and custom header-based authentication.
- Support for MCP tools and sampling, with additional MCP features coming soon.
Key features:
- Support for MCP tools
- Extend agents with MCP servers
- MCP servers hosting: serverless hosting and authentication support
Key features:
- Tool Integration: Theia AI enables AI agents, including those in the Theia IDE, to utilize MCP servers for seamless tool interaction.
- Customizable Prompts: The Theia IDE allows users to define and adapt prompts, dynamically integrating MCP servers for tailored workflows.
- Custom agents: The Theia IDE supports creating custom agents that leverage MCP capabilities, enabling users to design dedicated workflows on the fly.
Theia AI and Theia IDE’s MCP integration provide users with flexibility, making them powerful platforms for exploring and adapting MCP.
Learn more:
Key features:
- MCP servers are managed by Tome so there is no need to install uv or npm or configure JSON
- Users can quickly add or remove MCP servers via UI
- Any tool-supported local model on Ollama is compatible
<McpClient name=“TypingMind App” homepage=“https://www.typingmind.com” supports=“Tools” instructions={[ [“Public servers”, “https://docs.typingmind.com/model-context-protocol-(mcp)-in-typingmind”], [“Private servers”, “https://docs.typingmind.com/model-context-protocol-(mcp)-in-typingmind/use-mcp-with-private-mcp-connector”] ]}
TypingMind is an advanced frontend for LLMs with MCP support. TypingMind supports all popular LLM providers like OpenAI, Gemini, Claude, and users can use with their own API keys.
Key features:
- MCP Tool Integration: Once MCP is configured, MCP tools will show up as plugins that can be enabled/disabled easily via the main app interface.
- Assign MCP Tools to Agents: TypingMind allows users to create AI agents that have a set of MCP servers assigned.
- Remote MCP servers: Allows users to customize where to run the MCP servers via its MCP Connector configuration, allowing the use of MCP tools across multiple devices (laptop, mobile devices, etc.) or control MCP servers from a remote private server.
Learn more:
Key features:
- Visual to Code: Create high-fidelity UIs from your wireframes or mockups
- One-Click Deploy: Deploy with one click to a secure, scalable infrastructure
- Web Search: Search the web for current information and get cited results
- Site Inspector: Inspect websites to understand their structure and content
- Auto Error Fixing: Automatically fix errors in your code with intelligent diagnostics
- MCP Integrations: Connect to MCP servers from the Vercel Marketplace for zero-config setup, or add your own custom MCP servers
Learn more:
Key features:
- Support for stdio and server-sent events (SSE) transport
- Per-session selection of tools per agent session for optimal performance
- Easy server debugging with restart commands and output logging
- Tool calls with editable inputs and always-allow toggle
- Integration with existing VS Code extension system to register MCP servers from extensions
Key features:
- Connect to MCP servers over stdio; optional experimental RMCP/streamable HTTP support
- Configurable per-provider concurrency, startup/tool timeouts, and retries via
vtcode.toml - Pattern-based allowlists for tools, resources, and prompts with provider-level overrides
Learn more:
Key features:
- Agent Mode with MCP support: invoke tools and access data from MCP servers using natural language prompts
- Flexible server management: add and manage CLI or SSE-based MCP servers via Warp’s built-in UI
- Live tool/resource discovery: view tools and resources from each running MCP server
- Configurable startup: set MCP servers to start automatically with Warp or launch them manually as needed
Key features:
- Supports MCP tools
- SSE transport, full OAuth2 support
- Chat flow management for WhatsApp messages
- One click setup for connecting to your MCP servers
- In chat management of MCP servers
- Oauth flow natively supported in WhatsApp
<McpClient name=“Windsurf Editor” homepage=“https://codeium.com/windsurf" supports=“Tools, Discovery” instructions={[ [“Guide”, “https://docs.windsurf.com/windsurf/cascade/mcp”], [“Video tutorial”, “https://windsurf.com/university/tutorials/configuring-first-mcp-server”] ]}
Windsurf Editor is an agentic IDE that combines AI assistance with developer workflows. It features an innovative AI Flow system that enables both collaborative and independent AI interactions while maintaining developer control.
Key features:
- Revolutionary AI Flow paradigm for human-AI collaboration
- Intelligent code generation and understanding
- Rich development tools with multi-model support
Key features:
- Multiple MCP servers support
- Tool integration for executing commands and scripts
- Local server connections for enhanced privacy and security
- Easy-install from Smithery.ai
- Open-source, available for macOS, Windows and Linux
Key features:
- Prompt templates surface as slash commands in the editor
- Tool integration for enhanced coding workflows
- Tight integration with editor features and workspace context
- Does not support MCP resources
Key features:
- RepoGrokking - deep contextual understanding of codebases
- Agentic pipeline - runs, tests, and executes code before outputting it
- Zen Agents platform - ability to build and create custom agents and share with the team
- Integrated MCP tool library with one-click installations
- Specialized agents for Unit and E2E Testing
Learn more:
Adding MCP support to your application#
If you’ve added MCP support to your application, we encourage you to submit a pull request to add it to this list. MCP integration can provide your users with powerful contextual AI capabilities and make your application part of the growing MCP ecosystem.
Benefits of adding MCP support:
- Enable users to bring their own context and tools
- Join a growing ecosystem of interoperable AI applications
- Provide users with flexible integration options
- Support local-first AI workflows
To get started with implementing MCP in your application, check out our Python or TypeScript SDK Documentation