Heavy Tool Calling & MCP: Building Next-Gen AI Agents

8/11/2025

Here’s the thing, we’ve all been there. You’re building something with an LLM, pushing it to its limits, & you hit that invisible wall. The model is smart, sure, but it’s stuck in a box. It only knows what it was trained on, & the real world is happening right now, outside that box. You want it to check live inventory, book an actual appointment, or pull data from a proprietary database. That’s where the magic really begins.

We're moving past the era of simple chatbots that just regurgitate information. The future, & honestly the present, is about building agents – AIs that can do things. This is where two incredibly powerful concepts come into play: Heavy Tool Calling & the Model Context Protocol (MCP). And with models like GPT-5 on the horizon, understanding these is like getting a sneak peek at the developer's cheat codes for the next five years.

This isn't just another high-level overview. We’re going to do a deep dive. We'll unpack what "heavy" tool calling really means, the nitty-gritty challenges you'll face, & the best practices to overcome them. Then, we'll get into MCP, the unsung hero that’s standardizing how these AIs talk to the rest of the world. By the end of this, you’ll have an insider's map to building the next generation of truly useful AI systems.

Part 1: Understanding Heavy Tool Calling

What is Tool Calling, Anyway?

Before we get "heavy," let's start with the basics. Tool calling, which you might also hear called function calling, is honestly one of the biggest leaps we've seen in practical AI. At its core, it’s about giving an LLM access to external tools. Think of it like this: you wouldn't ask a brilliant mathematician to do complex arithmetic in their head when a calculator is sitting right there. The calculator is a tool that makes them faster & more accurate.

That’s exactly what we’re doing with LLMs. We give them tools to break out of their pre-trained knowledge box. These tools can be:

Simple functions: like a calculator, a currency converter, or a date formatter.
APIs: to fetch real-time information like weather forecasts, stock prices, or sports scores.
Databases: to query for specific information, like a customer's order history or product details.

The LLM doesn’t run the code itself. Instead, when a user asks a question, the model intelligently figures out that it needs a tool. It then generates a perfectly formatted JSON object with the name of the tool it wants to use & the arguments it needs. Your application takes that JSON, runs the actual function, & then feeds the result back to the LLM. The model then uses that new information to give the user a final, context-aware answer. Pretty cool, right?

So, What Makes it "Heavy"?

"Heavy Tool Calling" isn't an official term you'll find in a textbook. It's a phrase that captures where the industry is heading. We're moving beyond simple, one-off tool calls. We're building complex, multi-step agentic workflows where the AI might need to call dozens, or even hundreds, of different tools to accomplish a single goal.

Imagine a customer service bot on an e-commerce site. A user asks, "I want to return the blue shirt from my last order & see if the red one is available in a medium to ship to my office address by Friday."

A simple chatbot would fail spectacularly. But an agent with heavy tool calling capabilities would:

Call Tool #1 (Get User Info): Authenticate the user & pull up their account.
Call Tool #2 (Order History): Find the user's most recent order.
Call Tool #3 (Initiate Return): Start the return process for the blue shirt.
Call Tool #4 (Check Inventory): Look up the red shirt in a size medium.
Call Tool #5 (Shipping Estimator): Check if it can be delivered to the user's office address by Friday.
Call Tool #6 (Update Cart): Add the red shirt to the user's cart.

This isn't a single tool call; it's a dynamic, reasoned chain of actions. That's heavy tool calling. It’s the difference between a talking encyclopedia & a genuine digital assistant.

The Challenges of Juggling a Hundred Tools

As you can imagine, giving an AI a giant toolbox full of hundreds of tools creates some new, pretty significant challenges. It's not as simple as just dumping a list of functions into the prompt.

The Discovery Problem: How Does the LLM Find the Right Tool?

When an LLM has five tools to choose from, its decision is usually pretty accurate. When it has five hundred, things get messy. The model can get confused, pick the wrong tool, or even hallucinate arguments for a tool. This "tool selection" problem is a major hurdle. As the number of tools grows, the model's performance in choosing the right one can actually go down.

Context Window Congestion

Every tool you want the model to use needs a description, and those descriptions take up space in the context window. If you have hundreds of detailed tool definitions, you could easily use up a significant portion of your context length before the user has even asked a question. This leaves less room for the actual conversation, leading to the model "forgetting" earlier parts of the chat.

Speed & Latency Issues

Each tool call adds a round trip between your application & the LLM. A simple question can turn into a multi-second ordeal as the model "thinks," calls a tool, gets the result, thinks again, calls another tool, & so on. For user-facing applications like a website chatbot, this latency can be a deal-breaker.

Handling Failures & Errors

What happens when a tool call fails? Maybe an external API is down, or the model provided a badly formatted argument. Building robust error handling that can gracefully manage these failures, maybe even allowing the model to retry or choose a different tool, is incredibly complex. Debugging a chain of five nested tool calls is a headache you don't want to have without a plan.

Part 2: Best Practices for Heavy Tool Calling

Okay, so we've seen the challenges. The good news is, developers are already coming up with some seriously clever solutions. This is where you get to see the real insider knowledge in action.

Taming the Beast: Strategies for Managing Many Tools

Dynamic Tool Selection with RAG

This is probably the most effective strategy out there right now. Instead of giving the LLM all 500 of your tools at once, you only give it the most relevant ones for the current user query. How? With Retrieval-Augmented Generation (RAG).

Here's the workflow:

Create Embeddings: You take the name & description of every single one of your tools & use an embedding model to turn them into vectors. You store these vectors in a vector database.
User Asks a Question: When a user sends a message, you first embed their query into a vector as well.
Similarity Search: You then perform a similarity search in your vector database to find the top 5 or 10 tools whose descriptions are most semantically similar to the user's question.
Provide a Curated List: NOW you make your call to the LLM. But instead of providing all 500 tools, you only provide the 10 most relevant ones you just retrieved.

This dramatically reduces context window usage & makes it MUCH easier for the model to choose the correct tool from a short, curated list.

Layered Prompting & Multi-Agent Systems

Another powerful technique is to break down the problem into layers. Instead of one giant prompt that does everything, you create a chain of smaller, specialized prompts. For example, you might have:

Agent 1 (The Router): A powerful model like GPT-4o whose only job is to look at the user's query & decide which specialized agent should handle it.
Agent 2 (The Customer Service Agent): A smaller, fine-tuned model that only knows about customer service tools (get order, process return, etc.).
Agent 3 (The Product Expert Agent): Another small model that only has tools for checking inventory, finding product specs, etc.

This approach, sometimes called a multi-agent system, lets you use smaller, faster, & cheaper models for the bulk of the work, only calling on the big, powerful model for the initial complex reasoning.

Clear Descriptions are EVERYTHING

This might sound obvious, but it’s the most common mistake people make. The LLM's ability to choose the right tool is almost ENTIRELY dependent on how well you describe it. Your function's docstring is now part of your prompt.

Bad Description:
1def get_user(id): # gets a user
Good Description:
1def get_user_by_id(user_id: str) -> dict: """Fetches a user's complete profile information from the database based on their unique user ID. Use this to find a user's name, email, and shipping address."""

The good description tells the model what the function does, what the parameters mean, & most importantly, when to use it. Be verbose. Be explicit. It makes all the difference.

Building Robust Error Handling

Your code needs to be prepared for tool calls to fail. When you get an error back from a tool, don't just give up. Pass the error message back to the LLM in the next turn. Often, the model is smart enough to understand the error & try something different. For example, if it gets a "user not found" error, it might realize it needs to ask the user for their email address instead of their name. This self-correction is a key part of building resilient agents.

Part 3: Introducing the Model Context Protocol (MCP)

What the Heck is MCP & Why Should I Care?

Okay, so heavy tool calling is how we get an AI to do complex things. But that brings up a new problem: how do we connect all these tools to the AI in the first place? Right now, it's a bit of a mess. Every AI assistant needs a custom-built integration for every single tool it uses.

Enter the Model Context Protocol (MCP). MCP is an open standard, first open-sourced by Anthropic, that aims to be the "USB-C for AI." It's a standardized way for LLMs to connect to external data sources & tools. Instead of building a dozen custom integrations for your app, you build one MCP "server," & any MCP-compatible "client" (like an AI assistant) can instantly connect to it & use its tools.

This is a HUGE deal. It means you can build a tool once & have it be usable by Claude, a future version of ChatGPT, or any other AI that adopts the standard. It fosters an ecosystem where tools & AIs are interoperable.

How MCP Works: A Quick Peek Under the Hood

MCP is built on a simple client-server model.

Hosts/Clients: These are the applications where the LLM lives, like an AI assistant or a chatbot.
Servers: These are the applications that expose tools & data. Your application, with all its cool proprietary functions, would be an MCP server.

A server can offer three main things to a client:

Resources: File-like data the AI can read, like the contents of a document or the response from an API call.
Tools: Functions the AI can call, just like we discussed with tool calling.
Prompts: Pre-written templates to help users accomplish common tasks.

The communication happens over a standardized protocol, making it easy for different systems to talk to each other without needing custom code for every single connection.

The Real-World Impact of MCP

This isn't just a theoretical concept. Major companies are already building with MCP. We're seeing MCP servers for Jira, Stripe, Asana, Google Drive, Slack, & more. This means you can have an AI assistant that can create a task in Asana, search for a file in your Google Drive, & send a notification to a Slack channel, all using a standardized protocol.

For businesses looking to build sophisticated AI that can interact with their own internal tools, standards like MCP are a game-changer. It's the kind of technology that will power the next generation of AI chatbots, like those created with Arsturn, allowing them to not just answer questions from a knowledge base, but to actually do things for the customer, like check an order status or book a demo directly in the company's calendar.

Part 4: The Future is Here: GPT-5 & The Agentic Revolution

What We Can Expect from GPT-5

While it's always hard to predict the future, based on recent "announcements" & the clear trajectory of AI development, we can make some educated guesses about what a model like GPT-5 will bring to the table.

Unified Architecture: The days of choosing between a fast model (like GPT-4o) & a powerful reasoning model are likely numbered. GPT-5 is expected to feature a unified architecture that can intelligently switch between speed & deep thought depending on the complexity of the query.
Agentic Functionality: We can expect agent-like behavior to be a core feature, not an afterthought. The model will be designed from the ground up to perform multi-step tasks & autonomously call external tools.
Superior Reasoning: The core reasoning ability will be significantly better, leading to fewer hallucinations & a greater ability to understand complex, multi-part instructions.
Massive Tool Capacity: With a larger context window & better reasoning, GPT-5 will be far more capable of handling the "heavy tool calling" scenarios we discussed, potentially working with hundreds of tools seamlessly.

Tying it All Together: GPT-5 with Heavy Tool Calling & MCP

This is where it all comes together. Imagine a developer in the near future. They won't need to spend as much time on the complex workarounds we use today for heavy tool calling. GPT-5's native reasoning will handle much of the heavy lifting of planning & executing complex task chains.

And how will it connect to the world? Through standards like MCP. MCP will provide the universal, plug-and-play layer that allows GPT-5 to securely & reliably access data & tools from thousands of different services. The combination of a powerful, agentic model with a standardized communication protocol is the recipe for an explosion in AI capabilities. We'll see ecosystems of specialized agents that can collaborate to solve problems that are far beyond the reach of any single model today.

Building the Future of Business with AI

So what does this mean for businesses? It means the bar for customer interaction is about to be raised. SIGNIFICANTLY.

Imagine a website visitor asking a complex question. A future AI powered by GPT-5 could use heavy tool calling to check inventory in real-time, query a shipping provider for an accurate ETA, & even check the user's loyalty status to apply a personalized discount, all within a single, seamless conversation. This is the level of personalized, automated service that platforms like Arsturn are designed to deliver. By enabling businesses to build no-code AI chatbots trained on their own data, Arsturn helps them prepare for this agentic future, boosting conversions & providing a customer experience that feels truly intelligent. With Arsturn, businesses can create custom AI chatbots that provide instant customer support, answer questions, & engage with website visitors 24/7, laying the groundwork today for the even more powerful agentic systems of tomorrow.

The Final Word

Honestly, the next few years are going to be wild. Heavy tool calling is already pushing the boundaries of what's possible, but it comes with real complexity. MCP is emerging as the elegant standard we need to manage that complexity & create an interoperable ecosystem. And models like GPT-5 are poised to be the powerful engines that make it all accessible.

We're at a turning point where AI is evolving from a passive information source into an active partner that can help us accomplish our goals.

Hope this guide was helpful in mapping out what's coming. Let me know what you think.