Single MCP to Connect AI Agents with 600+ Tools

Today’s top AI Highlights:

1. Opensource platform to connect your AI agents to 600+ tools via MCP

2. AI agents can now run sandboxed code in just 197ms

3. MCP-powered agent in just 50 lines of code

4. Alibaba Qwen 3 models with MCP support and hybrid “thinking”

5. AI cybersecurity agent for secure vibe coding

& so much more!

Read time: 3 mins

AI Tutorial

Charts, diagrams, and visual data in PDFs remain a massive blind spot for most RAG systems. While text-based RAG has become relatively straightforward to implement, extracting meaningful insights from visual elements requires specialized approaches that many developers struggle to implement efficiently. The standard workaround of OCR followed by text embedding loses crucial context and fails completely with complex visual elements.

In this tutorial, we'll build a cutting-edge Vision RAG system that uses Cohere's Embed-4 model to create unified vector representations that capture both visual and textual elements. Then, we'll use Google's Gemini 2.5 Flash to analyze these retrievals and generate comprehensive answers by fully understanding the visual context.

We share hands-on tutorials like this every week, designed to help you stay ahead in the world of AI. If you're serious about leveling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.

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Latest Developments

First Agent-Native Cloud Infrastructure 🤖🗃️🔐

Daytona, the open-source infrastructure that provides a scalable runtime for executing AI-generated code and managing agent workflows, has released its cloud service. Daytona Cloud provides ultra-fast sandbox environments built specifically for AI agent workflows.

With environment creation taking just 71ms, this infrastructure lets your agents spin up containers, execute code, and clean up — all in under 200ms total. Daytona maintains state between executions and provides full compatibility with standard Docker containers, making it easy to integrate into existing systems without learning proprietary formats or configurations.

Key Highlights:

1. Lightning-fast execution environment - Create sandboxes in 71ms, run code in 67ms, and clean up in 59ms for a total execution time under 200ms. This speed makes it practical for agents to test multiple code paths and iterate rapidly without waiting for traditional cloud provisioning.

2. Built for how agents actually work - Agents need to manage multiple environments, refine code progressively, maintain state across iterations, and access file operations programmatically. Daytona gives them a clean API for all these tasks without human hand-holding.

3. Developer-friendly - Available as both Python and TypeScript SDKs with simple installation via pip or npm. No complex setup required, and the open-source architecture means you can customize it for your specific agent workflows.

4. Agent-optimized pricing - Pay only for what you use with per-second billing and millisecond precision. No minimum runtime commitments or hidden bandwidth charges, plus a free tier for early adopters who want to test it out.

Opensource Infra to Power Unified MCP Servers 🧰 ⚙️

ACI.dev is the open-source infrastructure layer for AI-agent tool-use. It connects your AI agents to 600+ tools through a single, unified infrastructure layer. This platform handles all the complex authentication tasks, providing multi-tenant auth, granular permissions, and dynamic tool discovery—all while giving you flexibility to access tools via direct function calls or through a unified Model Context Protocol (MCP) server.

Instead of spending days building separate OAuth flows for services like Google Calendar and Slack, ACI.dev manages the authentication headaches so you can focus on creating powerful AI agents that actually do things.

Key Highlights:

1. Multi-tenant auth - ACI.dev handles the complete authentication lifecycle for all your connected services, supporting OAuth2, API keys, and no-auth options. Your users can securely authorize AI agents to access their accounts without wrestling with token management, refresh mechanisms, or OAuth client setup.

2. Natural language permission - Control what your agents can do using simple human language instructions like "Don't send emails to people outside my organization" or "Only search for AI-related topics." These instruction filters block API execution when agents attempt actions beyond their intended purposes.

3. Dynamic tool discovery - Instead of overwhelming your LLM's context window with hundreds of function definitions, ACI.dev's unified MCP server exposes just two meta-functions that discover and execute the right tools based on user intent, reducing token usage while maintaining full access to all 600+ tools.

4. Framework-agnostic - Use the platform through a unified MCP server or via a lightweight Python SDK compatible with any AI agent framework. Both options give you secure access to all integrated tools without locking you into any specific model provider or agent architecture.

Quick Bites

The Hugging Face team has released Tiny Agents, showing how to build an MCP-powered agent in just 50 lines of code. The implementation demonstrates that once you have an MCP client connected to tools, creating a functional agent requires only a simple while loop to manage the interaction flow. You can try it immediately with a simple npx @huggingface/mcp-client command to experience a working agent that connects to file system and browser tools.

AI agents are great pair programmers, but this AI agent pairs with your users instead. Listen Labs, an AI startup, just released the first-of-its-kind agentic customer researcher that autonomously conducts voice and text interviews with real people and tells you exactly what they want and why. This AI platform conducts 1000s of calls in parallel and delivers executive summaries, themes, and video highlights within an hour, helping teams quickly uncover pain points, test messaging, and understand customer perspectives.

Companies can get started by requesting access on the Listenlabs.ai website and receive $1,000 in credits.

The Alibaba Qwen team has released open-weight Qwen 3 family of models, featuring two MoE models and six dense models ranging from 0.6B to 32B sizes, all supporting a hybrid thinking approach with dedicated "Thinking" and "Non-Thinking" modes. This new family builds on nearly double the training data of its predecessor and supports an impressive 119 languages and dialects, making it one of the most linguistically diverse AI systems available today.

• Qwen3's flagship model (MoE 235B) matches or outperforms state-of-the-art models like DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro despite having just 10% of the activated parameters.

• The models excel at coding and agentic tasks with an enhanced MCP support for seamless integration with external data sources and tools through a standardized interface.

• All models are available under Apache 2.0 license on Hugging Face, ModelScope, and Kaggle, with deployment support through frameworks like SGLang, vLLM, Ollama, and LMStudio.

Tools of the Trade

1. Strix: AI cybersecurity agent that tests applications for vulnerabilities while you code and fixes them in real-time. It lets you run security tests using plain language commands, works autonomously with its own toolset (proxy, shell, editor, browser), and can operate multiple instances simultaneously for faster testing.

2. Mirror AI: A desktop LLM agent that executes real actions in your digital environment rather than just providing text responses. It integrates with various platforms and tools to run terminal commands, manage files, send emails, schedule calendar events, query databases, and more.

3. MegaParse: A document parser that converts PDFs, Word docs, PPTs, and other formats into formats optimized for LLMs. It preserves document structure, including tables, headers, footers, images, and TOC. Offers both standard parsing and vision-based parsing through multimodal LLMs like GPT-4o and Claude.

4. Magnitude: Opensource testing framework that lets you write web app tests in natural language. It uses two AI agents: a reasoning agent to plan tests and a visual agent to execute them by directly interacting with the interface. Tests adapt to UI changes automatically, making test maintenance easier.

5. Awesome LLM Apps: Build awesome LLM apps with RAG, AI agents, MCP, and more to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos, and automate complex work.

Hot Takes

1. An MBA professor told me that his students use ChatGPT to write assignments and he uses it to assess them. So GPT here is the world’s most inefficient transmission protocol. ~
   Amjad Masad
   
   

2. I’ve never received so many resumes from Meta. ~
   Peyman Milanfar

That’s all for today! See you tomorrow with more such AI-filled content.

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