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@ -5,10 +5,9 @@ Thank you for your interest in contributing to the Model Context Protocol (MCP)
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## Getting Started
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1. Install the required tools:
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- Node.js
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- Git
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- [Mintlify CLI](https://www.npmjs.com/package/mintlify): `npm i -g mintlify`
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- Node.js
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- Git
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- [Mintlify CLI](https://www.npmjs.com/package/mintlify): `npm i -g mintlify`
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2. Fork and clone the repository
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3. Create a new branch for your changes
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4. Run `mintlify dev` to preview changes locally
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11
clients.mdx
11
clients.mdx
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@ -13,11 +13,13 @@ This page provides an overview of applications that support the Model Context Pr
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| [Zed][Zed] | ❌ | ✅ | ❌ | ❌ | ❌ | Prompts appear as slash commands |
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| [Sourcegraph Cody][Cody] | ✅ | ❌ | ❌ | ❌ | ❌ | Supports resources through OpenCTX |
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| [Firebase Genkit][Genkit] | ⚠️ | ✅ | ✅ | ❌ | ❌ | Supports resource list and lookup through tools. |
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| [Continue][Continue] | ✅ | ✅ | ✅ | ❌ | ❌ | Full support for all MCP features |
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[Claude]: https://claude.ai/download
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[Zed]: https://zed.dev
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[Cody]: https://sourcegraph.com/cody
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[Genkit]: https://github.com/firebase/genkit
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[Continue]: https://github.com/continuedev/continue
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[Resources]: https://modelcontextprotocol.io/docs/concepts/resources
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[Prompts]: https://modelcontextprotocol.io/docs/concepts/prompts
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@ -64,6 +66,15 @@ The Claude desktop application provides comprehensive support for MCP, enabling
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- Seamless interoperability with Genkit's existing tools and prompts
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- Works across a wide variety of GenAI models from top providers
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### Continue
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[Continue](https://github.com/continuedev/continue) is an open-source AI code assistant, with built-in support for all MCP features.
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**Key features**
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- Type "@" to mention MCP resources
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- Prompt templates surface as slash commands
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- Use both built-in and MCP tools directly in chat
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- Supports VS Code and JetBrains IDEs, with any LLM
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## Adding MCP support to your application
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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.
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@ -751,7 +751,7 @@ uvicorn.run(app, host="0.0.0.0", port=8000)
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return mock_forecast_response
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class AsyncClient():
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def __aenter__(self):
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async def __aenter__(self):
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return self
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async def __aexit__(self, *exc_info):
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@ -60,5 +60,8 @@
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],
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"footerSocials": {
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"github": "https://github.com/modelcontextprotocol"
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},
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"feedback": {
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"thumbsRating": true
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}
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}
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