Module 3: Advanced Inferencing: Tool Calling

In Module 2, you learned to constrain AI outputs to specific formats. Now ACME Corporation wants to take their AI customer support to the next level: enabling the AI to not just respond, but to take actions—looking up order status, checking inventory, or scheduling callbacks. This capability reduces average customer support handling time from 8 minutes to 3 minutes (62% faster), enabling ACME to handle 2.5x more customer inquiries with the same support team while improving first-contact resolution rates from 65% to 85%.

Tool calling (also known as function calling) allows the AI to recognize when it needs external data or actions, and generate structured function calls that your systems can execute. In this module, you'll configure tool calling and define custom tools for ACME's customer support scenarios. By implementing tool calling, ACME achieves $400,000 annual savings through improved agent productivity and reduced escalation rates.

Note

This module focuses on the tool calling pattern—how the AI generates function calls. Actual tool execution with human-in-the-loop approval is covered in Module 4 (MCP Integration).

Learning Objectives

By the end of this module, you'll be able to:

• Enable tool calling in vLLM Playground server configuration
• Understand tool calling parsers for different model families
• Define custom tools with proper function schemas
• Interpret AI-generated function calls and arguments
• Choose the appropriate model and parser for tool calling use cases

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Exercise 1: Enable Tool Calling

ACME's engineering team needs to configure the vLLM server to support tool calling. This requires enabling the feature and selecting the appropriate parser for the model being used.

You'll restart the server with tool calling enabled and understand the different parser options.

Prerequisites

• Module 1 completed (familiar with vLLM Playground)
• Access to vLLM Playground web UI

Understanding Tool Calling

Tool calling enables AI models to recognize when they need external data or capabilities, and generate structured requests (function calls) that your application can interpret and execute.

Important: Tool calling does NOT mean LLM executes tools

When tool calling is enabled, the AI model generates a JSON-formatted function call with arguments — but LLM itself does not execute any code or call external APIs. Your application (vLLM Playground in this case) is responsible for parsing the tool call, executing the actual function, and returning results to the model. This design ensures security and gives you full control over what actions are taken.

Understanding Tool Calling Parsers

Different model families use different formats for tool calling. vLLM supports several parsers:

Parser	Models	Format
llama3_json	Llama 3.x, Llama 3.1, Llama 3.2	JSON-based function calls
mistral	Mistral, Mixtral	Mistral's native tool format
hermes	Hermes, Qwen (with Hermes prompt)	Hermes-style function calling
auto (Auto-detect)	Various	Attempts to detect model type

Steps

1. Open the vLLM Playground web UI:

   Navigate to: http://localhost:7860

2. If a server is running, stop it first:

   • Click Stop Server in the Server Configuration panel

3. In the Server Configuration panel, configure tool calling:

   • Check Enable Tool Calling
   • Select a tool calling parser (or leave as "Auto-detect")

   

4. For this exercise, configure:

   Setting	Value
   Model	Qwen/Qwen2.5-3B-Instruct (or similar Qwen model)
   Enable Tool Calling	✓ Checked
   Tool Call Parser	hermes (or Auto-detect)

   Note

   Qwen models are open and don't require HuggingFace authentication. If using gated models like Llama, ensure you have HuggingFace access configured.

5. Click Start Server and wait for the model to load.

6. Monitor the server logs in the Server Logs panel for tool calling confirmation. Look for messages indicating tool calling is enabled and the model has loaded successfully.

✅ Verify

Confirm tool calling is enabled:

• Server started without errors
• "Enable Tool Calling" shows as active in the UI
• Server logs confirm tool calling parser loaded

Troubleshooting

Issue: "Tool calling not supported for this model"

Solution:

1. Use a model that supports function calling (Llama 3.x, Mistral, Qwen)
2. Check the model's documentation for tool calling support
3. Try a different parser setting

Issue: Server fails to start with tool calling enabled

Solution:

1. Check GPU memory—tool calling may require additional resources
2. Try a smaller model
3. Review server logs for specific errors

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Exercise 2: Define Custom Tools

With tool calling enabled, ACME needs to define the tools (functions) that the AI can invoke. Each tool has a name, description, and parameter schema that tells the AI when and how to use it.

You'll create customer support tools that the AI can call to assist customers.

Understanding Tool Definitions

A tool definition includes:

• name: Function identifier (e.g., get_order_status)
• description: When to use this tool (helps AI decide)
• parameters: JSON Schema defining expected arguments

Steps

1. In vLLM Playground, navigate to the Tools panel (🔧 icon in the toolbar).

   

2. Click Add Tool to define your first customer support function.

3. Create an order status lookup tool:

   {
     "type": "function",
     "function": {
       "name": "get_order_status",
       "description": "Look up the current status of a customer order. Use this when a customer asks about their order, shipping, or delivery.",
       "parameters": {
         "type": "object",
         "properties": {
           "order_id": {
             "type": "string",
             "description": "The order ID to look up (e.g., ORD-12345)"
           }
         },
         "required": ["order_id"]
       }
     }
   }
   

   

4. Add a second tool for customer information:

   {
     "type": "function",
     "function": {
       "name": "get_customer_info",
       "description": "Retrieve customer account information. Use this when you need to verify customer identity or look up account details.",
       "parameters": {
         "type": "object",
         "properties": {
           "customer_email": {
             "type": "string",
             "description": "Customer's email address"
           },
           "customer_id": {
             "type": "string",
             "description": "Customer's account ID (optional if email provided)"
           }
         },
         "required": ["customer_email"]
       }
     }
   }
   

5. Add a third tool for scheduling callbacks:

   {
     "type": "function",
     "function": {
       "name": "schedule_callback",
       "description": "Schedule a callback from a support agent. Use this when the customer requests to speak with a human or needs escalated support.",
       "parameters": {
         "type": "object",
         "properties": {
           "customer_phone": {
             "type": "string",
             "description": "Customer's phone number for callback"
           },
           "preferred_time": {
             "type": "string",
             "description": "Preferred callback time (e.g., 'morning', 'afternoon', '2pm EST')"
           },
           "issue_summary": {
             "type": "string",
             "description": "Brief summary of the customer's issue"
           }
         },
         "required": ["customer_phone", "issue_summary"]
       }
     }
   }
   

6. Add a product search tool:

   {
     "type": "function",
     "function": {
       "name": "search_products",
       "description": "Search the product catalog. Use this when a customer asks about products, availability, or pricing.",
       "parameters": {
         "type": "object",
         "properties": {
           "query": {
             "type": "string",
             "description": "Search query (product name, category, or keywords)"
           },
           "category": {
             "type": "string",
             "description": "Product category filter (optional)",
             "enum": ["electronics", "clothing", "home", "sports", "all"]
           },
           "max_results": {
             "type": "integer",
             "description": "Maximum number of results to return"
           }
         },
         "required": ["query"]
       }
     }
   }
   

7. Your tools panel should now show 4 defined tools:

   

✅ Verify

Confirm tools are properly defined:

• All 4 tools appear in the Tools panel
• Each tool has name, description, and parameters
• No JSON syntax errors (panel accepts the definitions)

Best Practices for Tool Definitions

Practice	Why It Matters
Clear descriptions	Helps AI decide when to use the tool
Specific parameter names	Reduces ambiguity in extracted values
Required vs optional	Guides AI on minimum needed information
Enum constraints	Limits values to valid options
Default values	Provides sensible fallbacks

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Exercise 3: Test Tool Calling Workflow

Now you'll see tool calling in action. The AI will analyze customer messages and generate appropriate function calls based on the tools you defined. Remember: in this module, we're observing the pattern—the AI generates the calls, but we don't execute them.

Steps

1. Ensure your tools from Exercise 2 are loaded and the server is running with tool calling enabled.

2. In the Chat panel, set a system prompt for customer support:

   You are a helpful customer support assistant for ACME Corporation. You have access to tools to help customers with their orders, account information, scheduling callbacks, and product searches. Use the appropriate tool when a customer needs specific information or actions.
   

3. In the Tools panel, set Tool Choice to Auto (recommended).

   This setting allows the AI to automatically decide when to use tools based on the conversation context.

4. Test order status inquiry:

   Hi, I placed an order last week and haven't received any updates. My order number is ORD-78432. Can you check the status?
   

   Expected AI behavior—generates a tool call:

   {
     "name": "get_order_status",
     "arguments": {
       "order_id": "ORD-78432"
     }
   }
   

   

   Note

   Streaming is disabled for tool calling due to a bug in vLLM v0.11.0. This issue has been resolved in later versions of vLLM.

5. Observe the response format:

   • The AI recognizes the need for external data
   • Instead of making up information, it generates a function call
   • The arguments are extracted from the customer's message

6. Test customer lookup:

   I need to update my shipping address. My email is john.smith@email.com
   

   Expected tool call:

   {
     "name": "get_customer_info",
     "arguments": {
       "customer_email": "john.smith@email.com"
     }
   }
   

   

7. Test callback scheduling:

   This is frustrating! I've been trying to resolve this billing issue for days. Can someone call me back? My number is 555-123-4567, preferably in the afternoon.
   

   Expected tool call:

   {
     "name": "schedule_callback",
     "arguments": {
       "customer_phone": "555-123-4567",
       "issue_summary": "Billing issue",
       "preferred_time": "afternoon"
     }
   }
   

   

8. Test product search:

   Do you have any wireless headphones under $100?
   

   Expected tool call:

   {
     "name": "search_products",
     "arguments": {
       "query": "wireless headphones",
       "category": "electronics",
       "max_results": 5
     }
   }
   

   

9. Test a message that doesn't need tools:

   Thanks for your help today!
   

   Expected: Normal text response (no tool call) — the AI should respond conversationally without invoking a tool.

10. Test multiple potential tools:

    I want to check on order ORD-99001 and also see if you have the new laptop model in stock.
    

    Observe: The AI may generate multiple tool calls or prioritize one. Different models handle this differently.

    

✅ Verify

Confirm tool calling workflow works:

• AI generates tool calls for appropriate requests
• Arguments are correctly extracted from customer messages
• Tool names match the defined functions
• AI responds normally when tools aren't needed
• JSON format is valid and parseable

Understanding the Workflow

┌─────────────────────────────────────────────────────────────┐
│                    Tool Calling Workflow                    │
│                                                             │
│  1. Customer Message                                        │
│     "Check order ORD-12345"                                 │
│              ↓                                              │
│  2. AI Analysis                                             │
│     Recognizes need for get_order_status tool               │
│              ↓                                              │
│  3. Tool Call Generated                                     │
│     {"name": "get_order_status", "arguments": {...}}        │
│              ↓                                              │
│  4. [Module 4] Tool Execution    (MCP Server)               │
│     Actual function runs, returns data                      │
│              ↓                                              │
│  5. [Module 4] AI Response                                  │
│     AI incorporates result into customer response           │
└─────────────────────────────────────────────────────────────┘

In this module, we completed steps 1-3. Module 4 (MCP Integration) covers steps 4-5 with actual tool execution.

Troubleshooting

Issue: AI responds with text instead of tool calls

Solution:

1. Verify tool calling is enabled in server config
2. Check that tools are properly loaded
3. Ensure system prompt mentions available tools
4. Try rephrasing the customer message more explicitly

Issue: AI generates wrong tool or arguments

Solution:

1. Improve tool descriptions to be more specific
2. Add examples in the description
3. Check parameter names are clear and unambiguous
4. Try a different bigger model (Llama 4 has improved tool calling)

Issue: Invalid JSON in tool call

Solution:

1. Check tool call parser matches model
2. Try "Auto-detect" parser setting
3. Some models may need specific prompt formatting

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Clean Up

Before proceeding to Module 4, stop the current vLLM server to prepare for MCP integration:

1. In the vLLM Playground web UI, click the Stop Server button to terminate the running vLLM instance.

2. Verify the server has stopped by checking that the server status shows "Stopped" or the Start Server button becomes available again.

Note

Module 4 requires restarting the vLLM Playground daemon service after installing MCP dependencies. Stopping the server now ensures a clean transition.

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Troubleshooting

Issue: Tool calls not appearing in response

Solution:

1. Verify "Enable Tool Calling" is checked
2. Restart server after enabling
3. Check server logs for tool-related errors

Issue: Model ignores defined tools

Solution:

1. Ensure tools are saved/loaded before chatting
2. Add system prompt explicitly mentioning tools
3. Use models known for good tool calling (Llama 3.1+, Mistral)

Issue: Performance degradation with tools

Solution:

1. Tool calling adds processing overhead
2. Reduce number of tools if possible
3. Use concise tool descriptions

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Learning Outcomes

By completing this module, you should now understand:

• ✅ How tool calling enables AI to request external data and actions
• ✅ The role of tool parsers for different model families
• ✅ How to define tools with proper schemas and descriptions
• ✅ The workflow from customer message to generated function call
• ✅ Best practices for tool definitions that guide AI behavior
• ✅ The difference between tool call generation and tool execution

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Module Summary

You've successfully completed the Advanced Inferencing: Tool Calling module.

What you accomplished:

• Enabled tool calling in vLLM Playground server configuration
• Understood tool calling parsers for Llama, Mistral, and Hermes models
• Defined 4 customer support tools with proper schemas
• Tested tool calling workflow with various customer scenarios
• Observed AI-generated function calls with extracted arguments

Key takeaways:

• Tool calling transforms AI from passive responder to active assistant
• Good tool descriptions are critical for AI decision-making
• The AI generates structured calls; your systems handle execution
• Different models have different tool calling capabilities
• Tool calling is the foundation for agentic AI workflows

Business impact for ACME:

• AI can now recognize when it needs customer data
• Structured tool calls integrate with existing backend APIs
• Reduces need for customers to provide information multiple ways
• Foundation for automated support workflows

Next steps:

Module 4 will explore Advanced Inferencing: MCP Integration - connecting the AI to actual external tools with human-in-the-loop approval for safe execution.

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Next: Module 4: MCP Integration — Connect the AI to actual external tools with human-in-the-loop approval.