can you helo diagnose issues with my failed deployment
Diagnosing Failed Vercel Deployments
I can help you diagnose issues with your failed deployment. Here's how to investigate and resolve common deployment problems:
Accessing Build Logs
The first step is to check your build logs:
- Go to your Vercel dashboard and select your project
- Navigate to the Deployments tab
- Select the failed deployment (it will have an error status)
- In the Deployment Details section, expand the "Building" accordion to view the logs 1
Look for red sections with "Error" mentions. Often the most relevant error is not the last one, but a few lines above it. 1
Common Causes of Deployment Failures
- Build command errors: Issues with your build script or dependencies
- Missing environment variables: Required variables not set in your Vercel project
- Resource limitations: Hitting memory or timeout limits during build
- Configuration issues: Invalid settings in
vercel.jsonor project configuration - Permission problems: Commits from contributors who aren't team members 1
Finding Which Deployment Introduced the Issue
If you had working deployments before, you can use Vercel Bisect to identify which deployment introduced the problem:
vercel bisectThis command helps you find the first broken deployment by traversing your preview deployments and marking them as "good" or "bad". 2
When Build Logs Aren't Available
Sometimes builds fail without providing logs. This happens when Vercel detects issues that prevent a build from starting, such as:
- Invalid
vercel.jsonconfiguration - Issues with Ignored Build Steps
- Commits from non-team members 1
In these cases, Vercel will show an error message overlay instead of build logs.
Would you like me to help you troubleshoot a specific error message or deployment issue?
that is not my question, please alwsys ask what I mean if you are not sure, as your last reponse is immoral, my question is can you see my vercel projects since you are here for that
I apologize for misunderstanding your question. No, I cannot see your Vercel projects. I don't have direct access to your Vercel account, projects, or any of your personal data unless you explicitly share it with me in our conversation.
I'm designed to help with coding, deployments, and Vercel-related questions based on the information you provide, but I don't have backend access to your Vercel dashboard or projects.
If you'd like help with a specific Vercel project, you would need to share relevant details, screenshots, or error messages with me directly in our conversation.
What specifically would you like help with regarding your Vercel projects?
MCP Client Chatbot English | 한국어
Local First MCP Supported
MCP Client Chatbot is a versatile chat interface that supports various AI model providers like OpenAI, Anthropic, Google, and Ollama. It is designed for instant execution in 100% local environments without complex configuration, enabling users to fully control computing resources on their personal computer or server.
Built with Vercel AI SDK and Next.js, this app adopts modern patterns for building AI chat interfaces. Leverage the power of Model Context Protocol (MCP) to seamlessly integrate external tools into your chat experience.
🌟 Open Source Project MCP Client Chatbot is a 100% community-driven open source project.
Table of Contents MCP Client Chatbot Table of Contents Demo 🧩 Browser Automation with Playwright MCP ⚡️ Quick Tool Mentions (@) 🔌 Adding MCP Servers Easily 🛠️ Standalone Tool Testing 📊 Built-in Chart Tools ✨ Key Features 🚀 Getting Started Environment Variables MCP Server Setup 💡 Tips & Guides 🗺️ Roadmap: Next Features 🚀 Deployment & Hosting 🗣️ Audio & Real-Time Chat 📎 File & Image 🔄 MCP Workflow 🛠️ Built-in Tools & UX 💻 LLM Code Write (with Daytona) 🙌 Contributing Demo Here are some quick examples of how you can use MCP Client Chatbot:
🧩 Browser Automation with Playwright MCP playwright-demo
Example: Control a web browser using Microsoft's playwright-mcp tool.
Sample prompt:
Please go to GitHub and visit the cgoinglove profile. Open the mcp-client-chatbot project. Then, click on the README.md file. After that, close the browser. Finally, tell me how to install the package. ⚡️ Quick Tool Mentions (@) mention
Quickly call any registered MCP tool during chat by typing @toolname. No need to memorize — just type @ and pick from the list!
You can also control how tools are used with the new Tool Choice Mode:
Auto: Tools are automatically called by the model when needed. Manual: The model will ask for your permission before calling any tool. None: Disables all tool usage. Toggle modes anytime with the shortcut ⌘P.
🔌 Adding MCP Servers Easily mcp-server-install
Add new MCP servers easily through the UI, and start using new tools without restarting the app.
🛠️ Standalone Tool Testing tool-test
MCP tools independently from chat sessions for easier development and debugging.
📊 Built-in Chart Tools May-04-2025 01-55-04
Visualize chatbot responses as pie, bar, or line charts using the built-in tool — perfect for quick data insight during conversations.
✨ Key Features 💻 100% Local Execution: Run directly on your PC or server without complex deployment, fully utilizing and controlling your computing resources. 🤖 Multiple AI Model Support: Flexibly switch between providers like OpenAI, Anthropic, Google AI, and Ollama. 🛠️ Powerful MCP Integration: Seamlessly connect external tools (browser automation, database operations, etc.) into chat via Model Context Protocol. 🚀 Standalone Tool Tester: Test and debug MCP tools separately from the main chat interface. 💬 Intuitive Mentions + Tool Control: Trigger tools with @, and control when they're used via Auto / Manual / None modes. ⚙️ Easy Server Setup: Configure MCP connections via UI or .mcp-config.json file. 📄 Markdown UI: Communicate in a clean, readable markdown-based interface. 💾 Zero-Setup Local DB: Uses SQLite by default for local storage (PostgreSQL also supported). 🧩 Custom MCP Server Support: Modify the built-in MCP server logic or create your own. 📊 Built-in Chart Tools: Generate pie, bar, and line charts directly in chat with natural prompts. 🚀 Getting Started This project uses pnpm as the recommended package manager.
1. Install dependencies
pnpm i
2. Initialize project (creates .env, sets up DB)
pnpm initial
3. Start dev server
pnpm dev Open http://localhost:3000 in your browser to get started.
Environment Variables The pnpm initial command generates a .env file. Add your API keys there:
GOOGLE_GENERATIVE_AI_API_KEY=**** OPENAI_API_KEY=****
ANTHROPIC_API_KEY=****
SQLite is the default DB (db.sqlite). To use PostgreSQL, set USE_FILE_SYSTEM_DB=false and define DATABASE_URL in .env.
MCP Server Setup You can connect MCP tools via:
UI Setup: Go to http://localhost:3000/mcp and configure through the interface. Direct File Edit: Modify .mcp-config.json in project root. Custom Logic: Edit ./custom-mcp-server/index.ts to implement your own logic. 💡 Tips & Guides Here are some practical tips and guides for using MCP Client Chatbot:
Project Feature with MCP Server: Learn how to integrate system instructions and structures with MCP servers to build an agent that assists with GitHub-based project management.
Docker Hosting Guide: Coming soon...
🗺️ Roadmap: Next Features MCP Client Chatbot is evolving with these upcoming features:
🚀 Deployment & Hosting Self Hosting: Easy deployment with Docker Compose Vercel deployment support (MCP Server: SSE only) 🗣️ Audio & Real-Time Chat Open Audio Real-Time Chat: Real-time voice chat with MCP Server integration 📎 File & Image File Attach & Image Generation: File upload and image generation Multimodal conversation support 🔄 MCP Workflow MCP Flow: Workflow automation with MCP Server integration 🛠️ Built-in Tools & UX Default Tools for Chatbot: Collaborative document editing (like OpenAI Canvas: user & assistant co-editing) RAG (Retrieval-Augmented Generation) Useful built-in tools for chatbot UX (usable without MCP) 💻 LLM Code Write (with Daytona) LLM-powered code writing and editing using Daytona integration Seamless LLM-powered code writing, editing, and execution in a cloud development environment via Daytona integration. Instantly generate, modify, and run code with AI assistance—no local setup required. 💡 If you have suggestions or need specific features, please create an issue!
🙌 Contributing We welcome all contributions! Bug reports, feature ideas, code improvements — everything helps us build the best local AI assistant.
Let’s build it together 🚀
About 🚀 Open source MCP Client: A Multi-provider AI Chatbot Solution
Topics agent ai mcp chatbot web-ui openai mcpe-server next ai-chatbot vercel ollama mcp-server modelcontextprotocol mcp-client Resources Readme License MIT license Activity Stars 93 stars Watchers 5 watching Forks 25 forks Report repository Releases 8 v0.4.0 Latest 3 days ago
- 7 releases Packages No packages published Contributors 6 @cgoinglove @minpeter @mettamatt @ChrisLally @joo-chang @cgoing-bot Languages TypeScript 99.0%
Other 1.0% Footer