Saba Zuberi

What is Function calling & MCP for LLMs? (explained with visuals and code) Before MCPs became popular, AI workflows relied on traditional Function Calling for tool access. Now, MCP is standardizing it for Agents/LLMs. The visual below explains how Function Calling and MCP work under the hood. Today, let's learn: - Function calling by building custom tools for Agents. - How MCPs help by building a local MCP client with mcp-use and using tools from Browserbase MCP server. In Function Calling: - The LLM receives a prompt. - The LLM decides the tool. - The programmer implements a procedure to accept a tool call request from the LLM and prepare a function call. The tool call request is found in the LLM's response when you prompt it. - A backend service executes the tool. This Function Calling takes place within our stack: - We host the tool. - We implement a logic to determine the tool to invoke and its parameters. - We execute it. So Function Calling requires us to wire everything manually. MCP simplifies this! Instead of hard-wiring tools, MCP: - Standardizes defining, hosting, and exposing tools. - Makes it easy to discover tools, understand schemas, and use them. - Demands approval before invoking them. - Detaches implementation from consumption. For instance, whenever you integrate an MCP server, you never write a line of Python code to integrate the tools. Instead, you just integrate the MCP server and everything beyond this follows a standard protocol handled by the MCP client and the LLM: - They identify the MCP tool. - They prepare the input argument. - They invoke the tool. - They use the tool’s output to generate a response. Everything happens through a standard (but abstracted) protocol. So here’s the key point: MCP and Function Calling are not in conflict. They’re two sides of the same workflow. - Function Calling helps an LLM decide what it wants to do. - MCP ensures that tools are reliably available, discoverable, and executable, without you needing to custom-integrate everything. For example, an agent might say, “I need to search the web,” using function calling. That request can be routed through MCP to select from available web search tools, invoke the correct one, and return the result. Check the workflow in the diagram below. In this setup, to build a local MCP client, I used mcp-use because it lets us connect any LLM to MCP servers & build private MCP clients, unlike Claude/Cursor. - Compatible with Ollama & LangChain - Stream Agent output async - Built-in debugging mode, etc Find the mcp-use GitHub repo in the comments! ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

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You're in a ML Inference Engineer interview at NVIDIA, and the interviewer asks: "Our team wants to switch from API to a self-hosted. Which serving framework and why?" Here's how you answer: Don't say: "vLLM is fastest" or "Ollama is easiest." Wrong framing. The real answer isn't about features - it's about matching serving philosophy to your constraints. Local prototype vs. production scale vs. complex workflows = completely different frameworks. NOTE - if you want to read this kinda content daily, consider subscribing my free newsletter - https://lnkd.in/g8ZJGsWj Most teams pick based on benchmarks, not bottlenecks Your serving choice matters less than your use case. PagedAttention isn't magic - it's just better memory management "Fastest" framework can be slowest for YOUR workload The infrastructure reality everyone misses: Single user prototype = Ollama/LLaMA.cpp wins Multi-user production = vLLM dominates AI agent workflows = SGLang is your only option Edge deployment = LLaMA.cpp or bust Architecture drives the decision, not features. "But what about performance?" Interviewer: "How do you handle 1000 concurrent users?" Performance without context is meaningless. > vLLM's PagedAttention gives you 24x throughput, but only if memory fragmentation was your bottleneck. > SGLang's RadixAttention only helps with cache reuse scenarios. By the way, just reading about inference frameworks won't help you land a job. I personally learnt a lot by doing stuff. If I were to do all over, I would rent a gpu from Hyperbolic for dirt cheap, and play with some open source model with different frameworks. The business framework that matters: > Rapid prototyping + Local dev = Ollama > Known high load + GPU budget = vLLM > Complex multi-step LLM workflows = SGLang > Constrained environments + Portability = LLaMA.cpp Match the tool to the business constraint. The evolution path most teams miss: > Start: Ollama (prototype fast) > Scale: vLLM (handle users) > Optimize: SGLang (complex workflows) > Edge: LLaMA.cpp (deploy anywhere) It's not replacement - it's graduation based on needs. The answer that gets you hired: "Framework choice isn't about better vs worse. It's about constraints vs capabilities. Ollama gives you speed-to-market. vLLM gives you user scale. SGLang gives you workflow complexity. Pick based on your biggest constraint, not your biggest benchmark." That's it for today. See ya tomorrow! And if you want to read more about these frameworks, checkout this blog: https://lnkd.in/eiH2sphA #llm #machinelearning #gpu #chatgpt #ai #agents

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The resurgence of reinforcement learning is great, but it’s missing the big picture. Chasing traditional RL is the wrong direction. In pursuing better policies and reward shaping, it’s easy to miss a simpler tool we already have: language. There is no reason to work in weights and gradients if you can work in natural language. Today we’re releasing our first set of research on Prompt Learning — not prompt tuning, not chain-of-thought. Something new. To our knowledge, no one has yet rigorously researched, characterized, and measured a full implementation of this approach. Instead of adjusting weights, we use MetaPrompting — where English evals and critiques (rather than just scalar metrics like previously done by the industry) drive targeted prompt updates. It’s a promising avenue of research, and the early results show that it works. Writeup in the comments.

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