Custom Tools & Tool Registry

Tools give agents the ability to take actions beyond text generation — querying databases, calling APIs, running calculations, or interacting with external systems. Without tools, an agent can only reason about information already in its context; with tools, it can retrieve live data and modify external state.

Beluga AI provides a composable tool system that lets you define custom tools, register them in a thread-safe registry, and pass them to agents for execution. Tools implement the tool.Tool interface and are created either manually or with the type-safe FuncTool generic wrapper.

Overview

The tool system consists of three components:

• tool.Tool interface — defines Name(), Description(), InputSchema(), and Execute()
• tool.FuncTool — a generic wrapper that turns a typed Go function into a Tool with auto-generated JSON Schema
• tool.Registry — a thread-safe, name-based collection for organizing and discovering tools

Agents receive tools at construction time via agent.WithTools() and invoke them during reasoning loops when the LLM produces a tool call.

Prerequisites

• Go 1.23 or later
• Beluga AI framework installed (go get github.com/lookatitude/beluga-ai)
• An LLM provider configured (any provider that supports tool calling)

Installation

go get github.com/lookatitude/beluga-ai

Creating Tools with FuncTool

The recommended approach uses tool.NewFuncTool with a typed input struct. The JSON Schema is generated automatically from struct tags.

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/lookatitude/beluga-ai/agent"
    "github.com/lookatitude/beluga-ai/llm"
    "github.com/lookatitude/beluga-ai/tool"

    _ "github.com/lookatitude/beluga-ai/llm/providers/openai"
)

// GreetInput defines the tool's input parameters.
// Struct tags control JSON Schema generation.
type GreetInput struct {
    Name string `json:"name" description:"The person to greet" required:"true"`
}

func main() {
    ctx := context.Background()

    // Create a typed tool with auto-generated schema
    greet := tool.NewFuncTool("greet", "Greets a person by name",
        func(ctx context.Context, input GreetInput) (*tool.Result, error) {
            return tool.TextResult(fmt.Sprintf("Hello, %s!", input.Name)), nil
        },
    )

    // Create the LLM
    model, err := llm.New("openai", llm.ProviderConfig{
        APIKey: "your-api-key",
        Model:  "gpt-4o",
    })
    if err != nil {
        log.Fatalf("llm: %v", err)
    }

    // Create an agent with the tool
    a := agent.New("assistant",
        agent.WithLLM(model),
        agent.WithTools([]tool.Tool{greet}),
    )

    result, err := a.Invoke(ctx, "Say hello to Alice")
    if err != nil {
        log.Fatalf("invoke: %v", err)
    }
    fmt.Println(result)
}

Implementing the Tool Interface Directly

For tools that need custom schema construction or special execution logic, implement tool.Tool directly.

package main

import (
    "context"
    "fmt"

    "github.com/lookatitude/beluga-ai/schema"
    "github.com/lookatitude/beluga-ai/tool"
)

// WeatherTool implements tool.Tool manually.
type WeatherTool struct{}

func (t *WeatherTool) Name() string        { return "get_weather" }
func (t *WeatherTool) Description() string { return "Returns current weather for a city" }

func (t *WeatherTool) InputSchema() map[string]any {
    return map[string]any{
        "type": "object",
        "properties": map[string]any{
            "city": map[string]any{
                "type":        "string",
                "description": "City name",
            },
        },
        "required": []string{"city"},
    }
}

func (t *WeatherTool) Execute(ctx context.Context, input map[string]any) (*tool.Result, error) {
    city, ok := input["city"].(string)
    if !ok || city == "" {
        return nil, fmt.Errorf("get_weather: city is required")
    }

    // In production, call a weather API here
    weather := fmt.Sprintf("Sunny, 22C in %s", city)
    return tool.TextResult(weather), nil
}

Using the Tool Registry

The tool.Registry provides thread-safe tool storage with name-based lookup and discovery.

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/lookatitude/beluga-ai/agent"
    "github.com/lookatitude/beluga-ai/tool"
)

func main() {
    ctx := context.Background()

    // Create a registry and register tools
    reg := tool.NewRegistry()

    greet := tool.NewFuncTool("greet", "Greets a person",
        func(ctx context.Context, input GreetInput) (*tool.Result, error) {
            return tool.TextResult(fmt.Sprintf("Hello, %s!", input.Name)), nil
        },
    )

    if err := reg.Add(greet); err != nil {
        log.Fatalf("register greet: %v", err)
    }
    if err := reg.Add(&WeatherTool{}); err != nil {
        log.Fatalf("register weather: %v", err)
    }

    // Discover registered tools
    names := reg.List() // sorted: ["get_weather", "greet"]
    fmt.Println("Available tools:", names)

    // Retrieve tools by name
    t, err := reg.Get("greet")
    if err != nil {
        log.Fatalf("get tool: %v", err)
    }

    // Collect all tools for an agent
    var agentTools []tool.Tool
    for _, name := range reg.List() {
        t, err := reg.Get(name)
        if err != nil {
            log.Fatalf("get tool %s: %v", name, err)
        }
        agentTools = append(agentTools, t)
    }

    // Pass tools to an agent
    a := agent.New("assistant",
        agent.WithTools(agentTools),
        // ... agent.WithLLM(model),
    )
    _ = a
}

Advanced Topics

Tool Middleware

Wrap tools with middleware to add cross-cutting concerns like logging, metrics, or timeouts.

import "github.com/lookatitude/beluga-ai/tool"

// Apply middleware to a tool
wrapped := tool.ApplyMiddleware(myTool,
    tool.WithTimeout(5 * time.Second),
    tool.WithLogging(logger),
)

Tool Hooks

Attach lifecycle hooks to observe tool execution events.

hooks := tool.Hooks{
    OnStart: func(ctx context.Context, name string, input map[string]any) error {
        log.Printf("tool %s called with %v", name, input)
        return nil
    },
    OnEnd: func(ctx context.Context, name string, result *tool.Result, err error) {
        if err != nil {
            log.Printf("tool %s failed: %v", name, err)
        }
    },
}

Validating Tool Input

Always validate input in Execute and return clear error messages. The LLM uses error responses to self-correct.

func (t *MyTool) Execute(ctx context.Context, input map[string]any) (*tool.Result, error) {
    query, ok := input["query"].(string)
    if !ok || query == "" {
        return tool.ErrorResult("query parameter is required and must be a string"), nil
    }
    // proceed with validated input
}

Context Timeouts for External Calls

When a tool calls external services, use context-based timeouts to prevent agent stalls.

func (t *APITool) Execute(ctx context.Context, input map[string]any) (*tool.Result, error) {
    ctx, cancel := context.WithTimeout(ctx, 10*time.Second)
    defer cancel()

    resp, err := t.client.Call(ctx, input)
    if err != nil {
        return nil, fmt.Errorf("api call: %w", err)
    }
    return tool.TextResult(resp.Body), nil
}

Configuration Reference

Component	API	Description
tool.NewFuncTool	NewFuncTool[I](name, desc, fn)	Create a typed tool from a Go function
tool.NewRegistry	NewRegistry()	Create an empty tool registry
Registry.Add	Add(t Tool) error	Register a tool (fails on duplicates)
Registry.Get	Get(name string) (Tool, error)	Retrieve a tool by name
Registry.List	List() []string	List all registered tool names (sorted)

Troubleshooting

Tool not found

Problem: Agent reports a tool is not available.

Solution: Verify the tool is registered in the registry and passed to the agent via agent.WithTools(). Use reg.List() to confirm registration.

Input schema mismatch

Problem: The LLM produces input that does not match the tool’s schema.

Solution: Ensure InputSchema() accurately describes the expected input. When using FuncTool, check that struct tags include json, description, and required annotations. Validate and normalize in Execute as a fallback.

Agent ignores available tools

Problem: The LLM generates text responses instead of calling tools.

Solution: Use an LLM that supports tool calling (e.g., GPT-4o, Claude). Verify tools are bound to the model via BindTools. Adjust the agent’s persona or system prompt to encourage tool use.

Related Resources

• MCP Tools Integration — Expose tools over the Model Context Protocol
• Tool System API Reference — Complete tool package documentation
• Working with Tools — Getting started guide