# Reducing Tool Calls in Agentic AI

> Source: <https://www.dotnetperls.com/2026_8_1_reducing-tool-calls-in-agentic-ai>
> Published: 2026-08-01 07:00:00+00:00

I have been experimenting with **optimizing** my local-LLM **agentic AI** workflow. Currently I have Gemma 4 E4B (other models work well too, but this one seems reliable and fast) as the LLM.

In testing my system, I found that a simple query, like "summarize the Kimi-K3 blog post" was slower than necessary because the agent had to **call multiple tools** to resolve the query. It had to find the date (with `find_blog_date`

or similar) and then `read_blog_post`

.

I decided to try to **reduce** and **combine tool calls**—the `read_blog_post`

MCP tool call could internally perform `find_blog_date`

. The instructions on the tools would be updated to note the new calling pattern. So I could resolve a query with 1 tool call instead of 2 tool calls (and this also reduces thinking and token use).

Basically, combining tool calls—such that a **single tool call** can resolve its own arguments—and not relying on the agent seems **beneficial** to **performance**. This makes the agentic AI workflow faster than before, reduces expensive GPU calls, and leads to greater user satisfaction.