Tool-Using Agents Explained

Common categories of agent tools:

Information retrieval:

• Database queries
• API calls to external services
• Web search
• Document retrieval (RAG)
• File reading

Actions/Mutations:

• Create/update/delete records
• Send emails or messages
• Trigger workflows
• Make purchases
• Update external systems

Computation:

• Code execution
• Data transformation
• Mathematical calculations
• File processing

Browser/UI:

• Navigate websites
• Fill forms
• Extract page content
• Take screenshots

Communication:

• Send notifications
• Post to Slack/Teams
• Create tickets
• Schedule meetings

The right tool set depends entirely on what your agent needs to accomplish.

Well-designed tools make agents more reliable:

Clear, specific descriptions: Bad: "Gets customer data" Good: "Retrieves customer profile including name, email, subscription status, and last login date. Requires customer_id."

Appropriate granularity:

• Too broad: "do_everything(request)" - LLM can't reason about it
• Too narrow: Hundreds of tiny tools overwhelm the LLM
• Just right: Coherent operations that make sense as units

Structured outputs: Return structured data (JSON) rather than prose. LLMs parse structure better than unstructured text.

Error handling: Return clear error messages that help the LLM recover:

• "Customer not found" vs. silent failure
• "Rate limited, retry in 60 seconds" vs. generic error
• Include what went wrong and what to try next

Idempotency where possible: Tools that can be safely retried if something fails mid-execution.

How agents decide which tools to use:

Tool selection by the LLM: The LLM sees available tools (names + descriptions) and decides:

• Which tool(s) to call
• What parameters to use
• In what order

Function calling: Modern LLMs have native function calling:

• Structured output format for tool calls
• Better than parsing from free-form text
• Reduces tool call errors significantly

Parallel vs. sequential:

• Some tasks benefit from calling multiple tools in parallel
• Others require sequential (result of A needed for B)
• Agent or orchestration layer decides

Tool execution flow:

1. LLM outputs tool call intent
2. Orchestration validates parameters
3. Tool function is executed
4. Results are formatted for LLM
5. LLM receives results and continues

Common failure modes:

• Wrong tool selected (improve descriptions)
• Bad parameters (add validation)
• Tool execution fails (handle errors gracefully)
• Results misinterpreted (structure outputs clearly)

The Model Context Protocol (MCP) and tool standardization:

What is MCP? Anthropic's open protocol for connecting LLMs to external tools and data sources. Standardizes how agents discover and use tools.

MCP benefits:

• Consistent interface across tools
• Tool discovery and documentation
• Reusable tool implementations
• Growing ecosystem of pre-built servers

Building your own tools: When to build custom:

• Internal APIs and databases
• Proprietary business logic
• Specific workflow requirements

When to use existing:

• Common services (Slack, GitHub, Google)
• Standard operations (web search, file handling)
• MCP servers already exist

Tool security:

• Validate all inputs before execution
• Scope permissions appropriately
• Log all tool calls for audit
• Rate limit to prevent abuse
• Sandbox dangerous operations (code execution)