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This page showcases applications that support the Model Context Protocol (MCP). Each client may support different MCP features:
FeatureDescription
ResourcesServer-exposed data and content
PromptsPre-defined templates for LLM interactions
ToolsExecutable functions that LLMs can invoke
DiscoverySupport for tools/prompts/resources changed notifications
InstructionsServer-provided guidance for LLMs
SamplingServer-initiated LLM completions
RootsFilesystem boundary definitions
ElicitationUser information requests
TasksLong-running operation tracking
AppsInteractive 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

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