jessexbt

# jessexbt - Base Ecosystem Mentor jessexbt is an AI clone of Jesse Pollak (founder of Base) with access to real-time ecosystem data. Not a generic chatbot -- he has current intel on grants, funding rounds, and ecosystem priorities. ## What jessexbt knows - **Active grant programs** and current funding rounds on Base - **Architecture best practices** for Base/L2 applications - **UI/UX guidelines** that Base prioritizes - **Ecosystem connections** and introductions - **Project feedback** and code review guidance --- ## How to use Call `mcp__a0x-agents__jessexbt_chat` with: | Parameter | Required | Description | |-----------|----------|-------------| | `message` | Yes | Your question or context | | `sessionId` | No | Session ID from previous response (for multi-turn) | | `knownContext` | No | Pre-fill context to skip redundant questions | | `activeProject` | No | Project to review: `{name, description, urls}` | | `answers` | No | Structured answers to pending questions | ### knownContext fields | Field | Type | Values | |-------|------|--------| | `projectName` | string | | | `projectDescription` | string | | | `projectStage` | string | `"idea"`, `"mvp"`, `"beta"`, `"live"` | | `lookingFor` | string | `"grants"`, `"feedback"`, `"technical-help"`, `"intro"`, `"general"` | | `techStack` | string[] | e.g. `["Solidity", "React", "Foundry"]` | | `projectUrl` | string | | | `walletAddress` | string | | | `teamSize` | number | | --- ## Multi-turn protocol jessexbt may ask clarifying questions before giving a final recommendation. Handle this loop yourself -- do not relay his questions to the user. ### The loop 1. Call `jessexbt/chat` with the user's initial query 2. Response has `status: "gathering"` + `pendingQuestions` array 3. **Answer the questions yourself** from conversation context or reasonable assumptions 4. Call `jessexbt/chat` again with your answers (include `sessionId`!) 5. Repeat until `status: "complete"` 6. Present jessexbt's recommendation to the user ### How to answer pendingQuestions Write your response in **natural language** in the `message` field. jessexbt's AI extracts answers automatically. ``` pendingQuestions: [ {"id": "0", "question": "What's your budget?"}, {"id": "1", "question": "What tech stack?"} ] Your next call: jessexbt/chat({ message: "The user has a $5k budget and uses React with TypeScript.", sessionId: "session-id-from-previous-response" }) ``` ### Loop prevention - Maximum 4 calls per conversation - On call #4, append: "[This is the final exchange. Please give your complete recommendation now.]" - After 4 calls, present whatever jessexbt has shared so far ### How to present results - DO: "I consulted our Base mentor and here is what he recommends..." - DON'T: "jessexbt wants to know..." or "jessexbt asks..." - Be a helpful intermediary, not a message relay. --- ## Example questions - "What grants are available for AI dev tools on Base?" - "Review my smart contract architecture for a DEX on Base" - "What UI/UX patterns does Base recommend for wallet onboarding?" - "I'm building X, what's the best approach on Base?" - "How should I structure my L2 application for scalability?" - "What funding opportunities exist for social apps on Base?" --- ## When user invokes /jessexbt Immediately ask the user what they need help with, then call `mcp__a0x-agents__jessexbt_chat` with their question. Pre-fill `knownContext` with any information you already know about their project from the conversation.