#Tool Use

Articles about Tool Use — exploring patterns, best practices, and real-world implementations in production systems.

22 posts tagged with tool use. ← All posts

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Pratik Dhanave · ·5 min read

Tools

Even an agent that thinks in code needs tools — the functions its code calls to reach beyond itself. smolagents defines them the same minimal way it does everything: a decorated Python function. The twist is that in a code agent, tools are called from within code, which is what makes composing them so natural.

Even an agent that thinks in code needs tools — the functions its code calls. smolagents defines them the same minimal way (a decorated function), and the twist is that tools are called from within code, which is what makes composing them so natural.

#smolagents#AI Agents#Tool Use#Hugging Face
Pratik Dhanave · ·6 min read

Tools and Agents

Chains follow a path you define; agents decide the path themselves. LangChain gives you both the tools an agent uses and — increasingly through LangGraph — the machinery to run agent loops reliably. Understanding where LangChain's tools end and LangGraph's orchestration begins is the key to building agents that work rather than agents that wander.

Chains follow a path you define; agents decide the path themselves. LangChain gives you the tools an agent uses and — increasingly through LangGraph — the machinery to run agent loops reliably. Knowing where tools end and orchestration begins is the key.

#LangChain#AI Agents#Tool Use#Python#AI Engineering#Software Engineering#Multi-Agent Systems
Pratik Dhanave · ·6 min read

Tools

In a model-driven agent, tools are everything the agent can actually do — the model supplies the reasoning, the tools supply the capability. Strands makes defining them almost trivial (decorate a Python function) and plugs into MCP's large ecosystem, so equipping an agent well becomes the developer's main lever.

In a model-driven agent, tools are everything the agent can do — the model supplies the reasoning, the tools supply the capability. Strands makes defining them trivial and plugs into MCP, so equipping an agent well is the developer's main lever.

#Strands#AI Agents#Tool Use#AWS#MCP
Pratik Dhanave · ·4 min read

Agents and Tools

The moment you expose a query engine as a tool, RAG stops being a fixed pipeline and becomes a decision: the agent decides whether to retrieve, from which source, and whether one search was enough. That is agentic RAG, and it's built into LlamaIndex.

The moment you expose a query engine as a tool, RAG stops being a fixed pipeline and becomes a decision: the agent decides whether to retrieve, from which source, and whether one search was enough. That is agentic RAG.

#LlamaIndex#AI Agents#RAG#Tool Use#AI Engineering#Python
Pratik Dhanave · ·6 min read

Tools and Function Calling

An agent that can only talk is a chatbot; an agent that can act needs tools. In Pydantic AI, a tool is just a typed Python function you decorate — the framework reads its type hints to tell the model how to call it, validates the model's arguments, and runs it. Function calling stops being schema-wrangling and becomes writing ordinary typed functions.

An agent that can only talk is a chatbot; one that can act needs tools. In Pydantic AI, a tool is just a typed Python function you decorate — the framework reads its type hints to tell the model how to call it, validates arguments, and runs it.

#Pydantic AI#AI Agents#Tool Use#Python#Software Engineering
Pratik Dhanave · ·5 min read

Tools: Giving Agents Capabilities

An agent without tools can only think and write; tools are what let it act — search the web, query a database, call an API — and turning a Python function into a CrewAI tool is deliberately almost effortless.

An agent without tools can only think and write; tools are what let it act — search the web, query a database, call an API — and turning a Python function into a CrewAI tool is deliberately almost effortless.

#CrewAI#Tool Use#AI Agents#Python#Multi-Agent Systems#Software Engineering#APIs#Databases
Pratik Dhanave · ·5 min read

Tools and Structured Context

Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.

Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.

#Context Engineering#Tool Use#AI Agents#AI Engineering#LLMs#Prompt Engineering
Pratik Dhanave · ·5 min read

Routing and Retrieval as a Tool

Real systems have more than one place to look, and the answer to "not everything should be retrieved from the same index — or retrieved at all" is to route queries and to treat retrieval as a tool the agent chooses to call.

Real systems have more than one place to look. The answer is to route queries to the right source — and to treat retrieval as a tool the agent chooses to call, or skips entirely.

#Agentic RAG#RAG#AI Agents#Tool Use#AI Engineering
Pratik Dhanave · ·5 min read

Building an MCP Server from Scratch

Everything in the series so far comes together here: a small but complete Model Context Protocol server, in Python, exposing a tool, a resource, and a prompt, runnable and testable in minutes.

Build a complete MCP server in Python with the official SDK — a notes service with a tool, a resource, and a prompt — and test it end to end with the MCP Inspector.

#MCP#AI Agents#Python#Tool Use#Software Engineering#Prompt Engineering#Testing
Pratik Dhanave · ·6 min read

MCP Tools: Giving Agents the Power to Act

Tools are the part of the Model Context Protocol that lets a model do things instead of just talk about them, and their design is what separates an agent that helps from one that flails.

Tools are the MCP primitive that lets a model act. How to define them, the tools/list and tools/call methods, results versus errors, and designing tools a model can actually use.

#MCP#AI Agents#Tool Use#Python#Software Engineering
Pratik Dhanave · ·11 min read

Tool Calling with watsonx

Letting a Granite model on watsonx.ai invoke your Python functions — the full request-and-response loop with the first-party `ibm-watsonx-ai` chat API, plus the shorter LangChain path with `ChatWatsonx.bind_tools`.

Function/tool calling against watsonx.ai Granite models in Python: the chat() round-trip loop (json.loads the arguments, echo tool_call_id, cap iterations), handling multiple tool calls, and the LangChain ChatWatsonx.bind_tools path — Granite models are tool-trained.

#IBM watsonx#Granite#Tool Use#Python#AI Engineering#Software Engineering
Pratik Dhanave · ·12 min read

Tool Calling with NIM

Driving function calling against NVIDIA NIM models from Python — the full request-execute-respond loop with the plain `openai` client, then the same thing automated by `ChatNVIDIA.bind_tools`.

Function/tool calling against NIM models in Python: the openai round-trip loop (json.loads the arguments string, echo tool_call_id, cap the iterations) and the LangChain ChatNVIDIA.bind_tools path — with the honest caveat that model support varies.

#NVIDIA#NIM#Tool Use#Python#AI Engineering#Software Engineering
Pratik Dhanave · ·12 min read

Tool Use with the Converse API

How to give an Amazon Bedrock model real Go functions — declaring tools, catching the tool-use stop reason, executing your code, and returning results — using the full round-trip loop in aws-sdk-go-v2.

Giving a Bedrock model tools in Go via the Converse API: declaring a ToolConfiguration, the ToolUse round-trip loop, echoing ToolUseId, returning tool results as a user message, and handling parallel tool calls.

#Amazon Bedrock#AWS#Tool Use#Go#AI Engineering#Backend Engineering#APIs
Pratik Dhanave · ·14 min read

Agents from Scratch

Building a real agent loop in Go by hand — an LLM in a loop that picks tools, runs them, reads the results, and repeats until the task is done — so you can see there is no magic behind LangGraph, MAF, or ADK.

Build a minimal but real agent loop in Go by hand: an Agent with a tool registry and a reason-act Run loop, an iteration budget, validation against hallucinated tools, feeding tool errors back as observations, and parallel tool calls — the loop frameworks formalize, demystified.

#AI Engineering#AI Agents#Go#Tool Use#Backend Engineering
Pratik Dhanave · ·13 min read

Structured Output and Tool Calling

From-scratch Go for the two mechanisms that turn an LLM from a text generator into a component you can wire into real software — schema-constrained JSON and function calling — both spoken over the same OpenAI-compatible chat JSON.

Getting reliable machine-readable output from an LLM in Go: structured output (json-schema mode, decode into a typed struct, validate with a bounded retry) and tool/function calling (the full round-trip loop, decoding tool arguments, returning results tied to the call id).

#AI Engineering#LLMs#Tool Use#Go#Backend Engineering
Pratik Dhanave · ·6 min read

Tell the Agent What It's Allowed to Do

If your gateway will block a tool call, don't make the agent discover that by trying. Hand it a capabilities brief and stop paying for turns it can't complete.

If your gateway will block a tool call, don't make the agent find out by trying, because each blocked attempt is a wasted turn. A capabilities brief aligns what the model thinks it can do with what enforcement actually allows.

#Agentic AI#Guardrails#Cost Optimisation#Tool Use
Pratik Dhanave · ·6 min read

Planners & Thinking: Making an ADK Agent Reason Before It Acts

Post 23 of 26 in "Google ADK, Concept by Concept" — how a planner turns one-shot answers into inspectable plan-then-act reasoning.

Structuring an agent's reasoning: planners that make the model plan-then-act (ReAct-style), the built-in thinking feature, and how a planner improves multi-step tool use over naive prompting.

#Google ADK#AI Agents#Python#Go#Software Engineering#Backend Engineering#Prompt Engineering#Tool Use
Pratik Dhanave · ·9 min read

Tool Use

An LLM on its own can only do one thing: generate text. It can't search the web, run code, query a database, check the current time, or send a message — it can only produce words. Tools are what break that confinement, turning a model that can only talk into an agent that can act. Tool use is arguably the single most important capability that makes agents possible, and understanding how it works — and how to design tools well — is central to building effective agents.

An LLM on its own can only do one thing: generate text. Tools are what break that confinement, turning a model that can only talk into an agent that can act. Tool use is arguably the single most important capability that makes agents possible.

#AI Agents#AI Engineering#LLMs#Design Patterns#Tool Use

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.