AI Tool

An AI tool is a capability beyond an LLM’s native text generation that an AI agent can invoke to access data, perform actions, or integrate with services. Tools can be connected in different ways: through direct API calls, plugin frameworks, or standardized protocols such as the Model Context Protocol (MCP).

Key characteristics

• Action-enabling – Executes tasks the LLM cannot perform directly (e.g., database queries, API calls, code execution,, file system access)
• Structured interface – Defined inputs, outputs, and error formats
• Discoverable – May be listed dynamically (e.g., via MCP) or hardcoded in integrations
• Interoperable – Works across systems when exposed through common standards like MCP
• Composable – Can be combined into multi-step workflows coordinated by an orchestration layer
• Permissioned – Access can be governed by security and policy rules

Also:

• Passive – Wait to be called, don’t initiate actions independently
• Functional – Designed to perform specific operations when invoked
• Specialized – Built for particular use cases or domains
• Variable predictability – Range from deterministic to highly variable outputs

A tool is not itself intelligent. Its power comes from being combined with AI agents, orchestration, and models into larger agentic or AI systems.

Tool behavior spectrum

Deterministic tools

• Calculator APIs – 2+2 always equals 4
• Database queries – Same query returns same data (at that moment)
• File operations – Reading a file returns the same content

Non-deterministic tools

• LLM-powered tools – Text summarization, content generation, analysis
• Agent-based tools – Research assistants, complex workflow automators
• AI services – Image generation, recommendation engines
• Dynamic systems – Weather APIs, stock prices, search results

Understanding the relationships

Tool vs. LLM

• LLM – Generates text, code, or reasoning outputs from prompts
• Tool – Executes actions or retrieves data the LLM cannot produce on its own

What LLMs can do

• Draft a document from a prompt
• Translate a sentence
• Generate code snippets
• Answer factual questions (within training data)

What Tools can do

• Query a live database
• Retrieve current web information
• Add events to a calendar
• Execute precise calculations

Key distinction: An LLM produces content; a tool performs operations or retrieves information beyond the model’s native scope.

Examples

In a travel-planning AI system:

1. The LLM processes the user’s request: “Find me a flight to Paris next week”
2. The AI agent (powered by the LLM) reasons: “I need to search for flight availability to Paris for next week”
3. LLMs with native function calling (GPT-4, Claude) directly generate structured tool calls to the flight search tool, which could be:
   • A direct API integration
   • Exposed by an MCP server
   • Available through a plugin system
4. The orchestration layer executes the tool call:
   • Manages the connection (API, plugin, or MCP client/server)
   • Handles retries and error recovery
   • Logs the interaction
5. The tool returns flight data to the orchestration layer
6. The LLM receives and interprets the results, generating a natural language response: “I found 3 flights to Paris next week. The best option is…”

This workflow transforms a static language model into an active system that can:

• Understand intent through natural language
• Make decisions about which tools to use
• Execute real-world actions
• Interpret and present results meaningfully

Non-examples

• The LLM itself – It generates predictions but does not expose callable actions
• Training data – Knowledge embedded in the model, not an invocable capability
• Static outputs – Prewritten text or fixed responses, which lack structured invocation

A tool must be an active capability that an agent can call, not just information or generation.