Weizhu Chen

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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Google Research Director Denny Zhou, who founded the LLM Reasoning Team at DeepMind, recently gave a great talk at Stanford’s CS25 class: Large Language Model Reasoning. This was one of the most intuitive talks about reasoning model training and application. He outlined new directions for training models to handle questions where answers aren’t easily verifiable. My team is currently working on an enterprise LLM project focused on using reasoning to extract relevant context and generate responses for unverifiable cases. It’s a tough but promising area, and it’s an interesting time to be building. [Link to Denny Zhou's talk: https://lnkd.in/gdJ6Mzi8]

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Why #SmallLanguageModels are the real future? ⚡ Most agents route every task, no matter how trivial - through GPT-4 or Claude → That’s expensive, slow, and overkill. ⚡ NVIDIA’s new paper argues→ #SLMs models small enough to run on consumer hardware are faster, cheaper, and just as capable for most agentic tasks. ⚡ Prioritize SLMs for cost-effective deployment → Cut latency, energy use, and infra costs, especially for real-time or on-device inference. ⚡ Design modular agentic systems → Use SLMs for routine, narrow tasks; reserve #LLMs only for complex reasoning. ⚡ Leverage SLMs for rapid specialization → Fine-tune small models quickly for evolving use cases, enabling faster iteration and easier adaptation. ⚡Agentic workloads are rarely open-ended conversations. They’re structured, repetitive, and scoped: ✸ Summarize a doc. ✸ Extract a field. ✸ Call an API. ✸ Generate boilerplate code. For these, you don’t need a trillion-parameter giant. A well-tuned 2–7B model often does the job just as well or better. ⚡ Proof in Numbers: ✸ Phi-3 Small (7B) rivals 70B on reasoning + code. ✸ DeepSeek-R1-Distill (7B) beats Claude 3.5 + GPT-4o on reasoning. ✸ SmolLM2 (≤1.7B) matches 70B models from 2 years ago. ✸ Nemotron-H (2–9B) delivers 30B-level tool use at a fraction of compute. ✸ Toolformer (6.7B) outperforms GPT-3 (175B) by learning APIs. Smaller ≠ weaker. ⚡The Efficiency Edge: ✸ 10–30× cheaper to run. ✸ Lower energy use + faster response. ✸ Overnight fine-tuning with LoRA/QLoRA. ✸ More reliable with strict formats (JSON, XML, Python). ✸ Deployable locally for cost savings + data control. ⚡Smarter Architectures: Don’t build monolithic agents. ✸ Use SLMs by default. ✸ Call LLMs only when absolutely necessary. This modular approach is cheaper, more controllable, and easier to debug. ⚡Migration path (LLM → SLM): ✸ Log usage data. ✸ Cluster recurring tasks. ✸ Fine-tune SLMs. ✸ Replace LLM calls. ✸ Iterate + refine. ⚡Real-world Results: ✸ MetaGPT → 60% of LLM calls replaceable. ✸ Cradle → 70%. ✸ Open Operator → 40%. ⚡ Why hasn’t Flipped yet: ✸ Billions sunk into LLM infra. ✸ Benchmarks biased toward generalist models. ✸ Less hype for SLMs But none of these are technical blockers. ⚡ The Takeaway: ✸ The future of agents isn’t bigger models. ✸ It’s smarter architectures built on SLMs. ✸ SLMs give you speed, control, and affordability.

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Excited about our research on scalable video conferencing, using techniques from software-defined networking. Paper with Oliver Michel, Satadal Sengupta, Hyojoon (Joon) Kim, and Ravi Netravali to appear at ACM SIGCOMM next month. https://lnkd.in/egHunh2x

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