Remote MCP Server for Cloudflare

Below is the extended flow, including the user interface (UI) layer and how it interacts with your MCP-enabled agent. 1Each numbered step aligns UI events with the underlying MCP protocol exchanges. --- ## 0. User ↔ UI 1. **User types message** * User enters text into a chat input box (“Summarize last quarter’s sales figures.”) and clicks **Send** (or presses Enter). 2. **UI emits event** * A `sendMessage()` handler packages the text into a client message and forwards it to the Agent. --- ## 1. UI → Agent (Client Layer) ```text UI.sendMessage("Summarize last quarter’s sales figures.") ``` * The client-side code (e.g. a React component) invokes the agent SDK’s `agent.receiveUserMessage(...)`. --- ## 2. Agent → Context Provider(s) 1. **Agent requests context** ```json { "type": "ContextFetch", "resource": "sales_database", "query": "Q1 2025 figures" } ``` 2. **Context services respond** ```json { "type": "ContextFetchResponse", "data": [ { "type": "text", "text": "Q1 2025 total revenue: $1.2M" }, { "type": "text", "text": "Q1 2025 units sold: 34,000" } ] } ``` --- ## 3. Agent Assembles & Sends Prompt to LLM * Combines system, user, and context messages into an OpenAI-compatible payload. * Invokes `chat.completions.create({ messages: […] , stream: true })`. --- ## 4. LLM → Agent (Streaming) * As each token arrives, the agent emits a client-side event: ```js agent.onToken(token => UI.appendToken(token)) ``` * **UI** appends tokens in real time to the chat window. --- ## 5. Detect & Execute Tool Calls 1. **LLM emits a tool invocation** ```json { "type": "tool_invocation", "tool": "generate_chart", "args": { … } } ``` 2. **Agent** pauses streaming to run the tool: ```js UI.showStatus("Running chart generation…") agent.invokeTool("generate_chart", args) .then(result => { UI.hideStatus() agent.injectToolResult(result) }) ``` 3. **Tool service** returns, e.g.: ```json { "type": "tool_result", "tool": "generate_chart", "data": "<image_url>" } ``` 4. **UI** displays the chart inline: ```js UI.displayImage("<image_url>") ``` --- ## 6. Final Assembly & UI Rendering * Once all tools have run, the agent emits an **assistant message** event: ```js agent.onAssistantMessage(msg => UI.renderMessage(msg) ) ``` * **UI** renders the final text, along with any embedded images or interactive components. --- ## Full Sequence Diagram (with UI) ``` User UI Agent ContextSvc LLM ToolSvc | | | | | | |--type--> | | | | | | & Send |--sendMsg----->| | | | | | |--ContextFetch-----> | | | | | |<--ContextResp-------| | | | | |--ChatRequest------------------------->| | | | | |<--ChatStream--->| | | |<--appendToken(token)------------StreamTokens--------->| | | | | Detect tool call | | | | |--showStatus-->| | | | | | |--ToolInvoke-------------------------------------------->| | | |<--ToolResult--------------------------------------------| | |<--hideStatus & displayImage---------------------------------------------| | | | Merge & finalize response | | | |<--renderMessage(assistant)------------------------------| |<--sees reply with chart and text-----------------------------------------------------| ``` ### UI Components & Handlers * **ChatWindow** * `appendToken(token: string)` * `renderMessage(msg: { role, content, attachments })` * **InputBox** * `onSubmit(text: string)` * **StatusIndicator** * `show(text: string)` / `hide()` * **ToolOutput** * `displayImage(url: string)` * `displayTable(data: any[])` This layered flow ensures that **user actions** and **visual feedback** stay tightly synchronized with every underlying MCP and LLM exchange.