基于 AI 的 AO 工具调用类固醇，使用 MCP 服务器与您现有的工具和编辑器集成。
https://github.com/user-attachments/assets/3484e2db-e7cb-479a-84a2-0b399e1149ac
仅使用自然语言运行 AO 代码
完全使用自然语言创建并运行自定义 AO 代码/蓝图
与您现有的 AI 开发工具（如 Cursor、Windsurf、Claude 以及任何支持 MCP 工具调用的工具）集成
可以测试它推送到进程的所有代码
可以创建和测试复杂的处理程序
Related MCP server: Processing MCP Server
与 AO 生态系统工具集成
通过添加更多关于 AO 的上下文来提高代码生成能力
AO
Arweave
MCP TypeScript SDK
打字稿
Node.js
目前有两种安装和使用 Flux 的方法，目前仅在 Cursor 上进行过大量测试，因此我们将展示如何在 Cursor 中安装 FLux -
本地设置 - 对于希望所有内容都存在于本地的用户，无需涉及远程服务器
确保安装了最新稳定版本的 NODE.js - Node.js 下载
打开 Cursor，前往**“设置”** > “Cursor 设置” > “MCP” > “添加新的 MCP 工具”
将此代码粘贴到文件中
"mcpServers": {
"flux": {
"command": "npx",
"args": ["-y", "flux-ao@latest"],
}
}如果您正确地完成了所有操作，您将能够看到装有所有工具的 flux MCP，并且可以在 Cursor 中使用（PS：您可能需要多次重新加载 MCP 或重新启动 Cursor）！
远程设置 - 适用于想要使用 Flux 但不在本地安装任何内容的用户
打开 Cursor，前往**“设置”** > “Cursor 设置” > “MCP” > “添加新的 MCP 工具”
将此代码粘贴到文件中
"mcpServers": {
"flux": {
"url": "https://flux-2esw.onrender.com/sse",
}
}如果您正确地完成了所有操作，您将能够看到装有所有工具的 flux MCP，并且可以在 Cursor 中使用（PS：您可能需要多次重新加载 MCP 或重新启动 Cursor）！
我们建议您先在 Cursor 中添加 AO 文档的llms.txt文件，并将其用作上下文，然后再开始使用 MCP 服务器。如果您不知道如何操作，请查看此文档，了解如何将文档添加到 Cursor。
您为光标提供的上下文越多，响应就越准确。
现在，在您的 Cursor 项目中添加这些自定义规则，以确保 Cursor 不会出现幻觉或给出错误响应。您可以在**“设置”** > “Cursor 设置” > “规则” > **“添加新规则”**中添加这些规则。
For adding json capabilities ONLY IF NEEDED, you need to add a line "local json = require("json")" on top of file. BUT DONT USE IT UNLESS NEEDED. SIMPLE THINGS CAN BE DONE USING AO PROCESS STATE
Always use Send instead of msg.reply
Always make sure a handler is sending out a response/reply (using Send) and send it as data as well instaed of just returning using tags
Never add any tags by yourself, always add tags when needed or instructed by user, also {"Action":"Eval"} tag is for running lua in an ao process and {"Action" : "action_name"} is for running a handler
Never add the "Type" tag to anything, thats reserved for internal ao specifications
Always use Handler.utils whever possible when creating a handler, for example --
Handlers.add(
"pingpong",
Handlers.utils.hasMatchingTag("Action", "Ping"),
function (msg)
Handlers.utils.reply("Pong")(msg) -- or use Send() here
end
)现在您可以开始在 Cursor Agents 中使用 Flux MCP 服务器。
如果您发现服务器有任何问题或遇到任何错误，请通过打开问题或发送邮件至flux.mcp@gmail.com告知我们
Inputs: a (required), b (required).
Inputs: packageName (required), processId (required).
Inputs: handlerCode (required), processId (required).
Inputs: handlerCode (required), processId (required).
Inputs: processId (required).
Inputs: processId (required), url (required).
Inputs: blueprintCode (required), processId (required).
Inputs: blueprintName (required), processId (required).
Inputs: processId (required).
Inputs: data (required), handlerName (required), processId (required).
Inputs: code (required), processId (required), tags.
Inputs: data (required), processId (required), tags.
Inputs: tags (required), needsSqlite.
Inputs: transactionId (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 14 tools
Multiple tools have unclear boundaries and overlapping purposes. For example, 'load-blueprint', 'load-local-blueprint', 'load-official-blueprint', and 'load-token-blueprint' all seem to handle blueprint loading but with unspecified distinctions, while 'create-handler' and 'create-sqlite-based-handler' appear to be variants of handler creation without clear differentiation. The lack of descriptions exacerbates ambiguity, making it difficult for an agent to reliably choose between these tools.
The naming conventions are mixed but still readable. Most tools use kebab-case (e.g., 'apm-install', 'list-available-handlers'), which is consistent, but there are deviations like 'add' and 'spawn' which are single words without hyphens, and 'run-handler-using-handler-name' is overly verbose. While not chaotic, the pattern is inconsistent, affecting predictability.
With 14 tools, the count is reasonable for a server likely focused on process or workflow management, as inferred from tool names like 'spawn', 'run-lua-in-process', and 'send-message-to-process'. It's slightly on the higher side but not excessive, suggesting a well-scoped set that covers various operations without being overwhelming.
Significant gaps exist due to the lack of descriptions, making it impossible to fully assess coverage. Inferred from tool names, the domain seems to involve handlers, blueprints, and processes, but there's no clear CRUD lifecycle—for example, tools like 'add' and 'transaction' are vague, and operations like updating or deleting handlers or blueprints are missing. This incompleteness will likely cause agent failures in handling common workflows.
MCP server that lets your AI assistant build and deploy apps, hosting each one at a live URL.
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
MCP server for AI dialogue using various LLM models via AceDataCloud
