AI GLOSSARY

Function Calling

Function Calling enables LLMs to call functions and APIs in a controlled manner—from database queries to sending emails to interacting with ERP systems. It is the foundation of all AI agents and the key to true process automation with LLMs.

Schedule a Free Initial Consultation

✓ 80+ AI experts ✓ 25+ years of technology expertise ✓ ISO-certified ✓ Made in Germany

Core Components
Function, Schema, Call, Response

Use Cases
in Enterprise Environments

Standards
OpenAI Tools, Anthropic Tools, MCP

Best Practices
for Secure Function Calling

From Chat to Action

LLM doesn't just respond—it takes action within systems.

Structure Instead of Free-Form Text

Answers in a structured format—not as continuous text.

Security & Traceability

Every request is logged and verifiable.

Scalable

A model with many tools—more flexible than specialized applications.

Basics for Agents

Multi-step tasks involving multiple tool calls.

Standardized

OpenAI, Anthropic, and MCP have established similar standards.

What is Function Calling?

Function calling (also known as tool use ) refers to the ability of large language models to call defined functions—with structured arguments—and incorporate their results into their response.

The process: The developer describes available functions (e.g., “search for a customer,” “send an email”) and their parameters to an LLM. When a user makes a request, the model decides which function to call and with which arguments. It does not respond on its own—instead, it returns a structured call instruction. The application executes the function and passes the result back to the model.

Leading providers offer similar APIs: OpenAI Tools, Anthropic Tool Use, and Google Gemini Function Calling. The MCP (Model Context Protocol) standard is Anthropic’s proposal for interoperability.

For businesses, Function Calling is the foundation of all AI agents. Without Tool Use, every LLM remains limited to textual knowledge—with Tool Use, it becomes an active assistant.

JSON Schema

Functions are defined using JSON Schema—the standard for argument structure.

OpenAI Tools

OpenAI's API for function calling — the de facto standard.

Anthropic Tool Use

Claude's version — very similar structure, minimal differences.

Gemini Function Calling

Google's implementation — can also be combined with structured outputs.

Model Context Protocol (MCP)

Standard Protocol for Tool Integration — Cross-Platform.

Parallel Tool Use

Call multiple functions at the same time — reduces latency.

Structured Output

Enforce responses in JSON Schema — even without a function call.

Streaming Function Calls

Arguments are streamed during generation — faster user experience.

Best Practices for Function Calling

These six principles help ensure productive function-calling applications:

Clear function descriptions: The description determines whether the model selects the correct function.
Few, well-chosen tools: Too many functions confuse the model—focus leads to precision.
Idempotent functions: Multiple calls should not have unintended side effects.
Secure critical actions: Use approvals and human-in-the-loop checks for sensitive tasks.
Validate before execution: Check arguments—don’t trust them blindly.
Log all calls: Essential for auditing and troubleshooting.

Approach 1

Native Function Calling

OpenAI, Anthropic, and Gemini have their own APIs. Native, mature, but not 100 percent portable.

Standard

Approach 2

MCP Standard

Anthropics Model Context Protocol. Standardized tool integration—cross-platform.

Future

Approach 3

Custom Orchestration

Custom layers (LangChain, LlamaIndex) via native APIs. Flexible, but requires maintenance.

For Complex Scenarios

Common Mistakes in Function Calling

We see these pitfalls time and time again:

Too many functions: 50+ tools — the model loses track, and the error rate rises.
Unclear descriptions: The model selects the wrong function — the user receives the wrong answer.
Lack of validation: Unchecked arguments are executed — security risk.
Critical actions without approval: Deletions or bookings executed directly from the model — risky.
No error handling: Tool fails, model invents a response — inconsistent UX.