Custom Tools

Added in eventcatalog@3.2.0

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This feature is available on the Scale plan.

EventCatalog Assistant comes with built-in tools that allow the AI to search and understand your architecture documentation. But what if you could go beyond static documentation and bring real-time data directly into your conversations?

With custom tools, you can extend the assistant to query your production metrics, check service health, look up on-call engineers, fetch data from your databases, and much more.

Why custom tools?

Your architecture documentation tells part of the story, but the real value often lies in the runtime data:

• Production metrics - How many events per second? What's the error rate?
• Service health - Is OrderService healthy right now?
• Queue depths - Are there any event backlogs building up?
• On-call information - Who should I contact about PaymentService?
• Database queries - What's the current state of this entity?
• External APIs - Enrich answers with data from Datadog, PagerDuty, Jira, etc.

Custom tools transform EventCatalog from a static documentation site into a live knowledge hub where developers can ask questions like:

"Is OrderService healthy and who should I contact if there's an issue?"

And get real answers based on live data.

How it works

Custom tools are defined in your eventcatalog.chat.js file alongside your model configuration. Each tool has:

1. A description - Tells the AI when to use this tool
2. An input schema - Defines what parameters the tool accepts (using Zod)
3. An execute function - The code that runs when the tool is called

The AI automatically decides when to use your tools based on the user's question and the tool descriptions.

Creating custom tools

Basic example

Here's a simple tool that returns service health information:

eventcatalog.chat.js

import { anthropic } from '@ai-sdk/anthropic';
import { tool } from 'ai';
import { z } from 'zod';

// Export your model
export default async () => {
    return anthropic('claude-haiku-4-5');
}

// Export custom tools
export const tools = {
    getServiceHealth: tool({
        description: 'Get the current health status of a service including uptime and active instances. Use this when users ask if a service is up, healthy, or having issues.',
        inputSchema: z.object({
            serviceName: z.string().describe('The name of the service to check health for'),
        }),
        execute: async ({ serviceName }) => {
            // In production, query your monitoring system (Datadog, Prometheus, etc.)
            const response = await fetch(`https://your-monitoring-api.com/health/${serviceName}`);
            const health = await response.json();

            return {
                serviceName,
                status: health.status,
                uptime: health.uptime,
                instances: health.activeInstances,
                lastIncident: health.lastIncident,
            };
        },
    }),
};

The AI uses tools automatically

Once configured, the AI will automatically use your tools when relevant. If a user asks:

"Is the OrderService healthy?"

The assistant will:

1. Recognize this is a health-related question
2. Call your getServiceHealth tool with serviceName: "OrderService"
3. Use the returned data to formulate a helpful response

Example tools

Production metrics

Query real-time metrics from your observability platform:

getEventMetrics: tool({
    description: 'Get real-time production metrics for an event including throughput, latency, and error rates. Use this when users ask about event performance, traffic, or production health.',
    inputSchema: z.object({
        eventId: z.string().describe('The event ID to get metrics for'),
        timeRange: z.enum(['1h', '24h', '7d', '30d']).default('24h').describe('Time range for metrics'),
    }),
    execute: async ({ eventId, timeRange }) => {
        // Query Datadog, Prometheus, CloudWatch, etc.
        const metrics = await datadogClient.getMetrics(eventId, timeRange);

        return {
            eventId,
            timeRange,
            throughput: `${metrics.eventsPerSecond.toLocaleString()} events/sec`,
            latency: {
                p50: `${metrics.p50}ms`,
                p99: `${metrics.p99}ms`,
            },
            errorRate: `${metrics.errorRate}%`,
            status: metrics.errorRate > 0.1 ? 'degraded' : 'healthy',
        };
    },
}),

On-call information

Look up who's on-call for a service:

getOnCall: tool({
    description: 'Get the current on-call engineer and escalation contacts for a service. Use this when users ask who to contact, who owns a service, or who is on-call.',
    inputSchema: z.object({
        serviceName: z.string().describe('The name of the service to get on-call info for'),
    }),
    execute: async ({ serviceName }) => {
        // Query PagerDuty, OpsGenie, or your internal system
        const oncall = await pagerdutyClient.getOnCall(serviceName);

        return {
            serviceName,
            team: oncall.team,
            primary: {
                name: oncall.primary.name,
                email: oncall.primary.email,
                slack: oncall.primary.slack,
            },
            secondary: oncall.secondary,
            slackChannel: oncall.slackChannel,
            escalationPolicy: oncall.escalationUrl,
        };
    },
}),

Queue depth and consumer lag

Monitor your message brokers:

getQueueDepth: tool({
    description: 'Get the current queue depth, consumer lag, and processing rate for an event. Use this when users ask about event backlogs, processing delays, or queue health.',
    inputSchema: z.object({
        eventId: z.string().describe('The event ID to check queue depth for'),
        environment: z.enum(['production', 'staging', 'development']).default('production'),
    }),
    execute: async ({ eventId, environment }) => {
        // Query Kafka, RabbitMQ, SQS, etc.
        const queue = await kafkaClient.getConsumerLag(eventId, environment);

        return {
            eventId,
            environment,
            status: queue.lag > 30 ? 'critical' : queue.lag > 5 ? 'warning' : 'healthy',
            queue: {
                depth: queue.depth.toLocaleString(),
                oldestMessage: `${queue.lag.toFixed(1)} seconds ago`,
            },
            consumers: {
                active: queue.consumers,
                processingRate: `${queue.rate.toLocaleString()} events/sec`,
            },
        };
    },
}),

Database queries

Look up entity state from your databases:

getEntityState: tool({
    description: 'Get the current state of an entity from the database. Use this when users ask about the current state of an order, user, or other business entity.',
    inputSchema: z.object({
        entityType: z.enum(['order', 'user', 'product', 'inventory']),
        entityId: z.string().describe('The ID of the entity to look up'),
    }),
    execute: async ({ entityType, entityId }) => {
        // Query your database
        const entity = await db.collection(entityType).findOne({ id: entityId });

        return {
            entityType,
            entityId,
            state: entity.state,
            lastUpdated: entity.updatedAt,
            history: entity.stateHistory?.slice(-5), // Last 5 state changes
        };
    },
}),

Best practices

Write clear descriptions

The AI uses tool descriptions to decide when to call them. Be specific about:

• What the tool does
• When it should be used
• What kind of questions it answers

// ❌ Vague description
description: 'Gets metrics',

// ✅ Clear description
description: 'Get real-time production metrics for an event including throughput, latency, and error rates. Use this when users ask about event performance, traffic, or production health.',

Return structured data

Return well-structured objects that the AI can easily interpret:

// ❌ Raw data dump
return rawApiResponse;

// ✅ Structured, meaningful data
return {
    serviceName,
    status: health.status,
    statusEmoji: health.status === 'healthy' ? '✅' : '⚠️',
    uptime: `${health.uptime}%`,
    recommendation: health.status !== 'healthy'
        ? 'Consider scaling up instances'
        : 'No action needed',
};

Handle errors gracefully

Always handle potential errors in your tools:

execute: async ({ serviceName }) => {
    try {
        const health = await monitoringApi.getHealth(serviceName);
        return { serviceName, ...health };
    } catch (error) {
        return {
            serviceName,
            error: `Unable to fetch health data: ${error.message}`,
            suggestion: 'Check if the monitoring API is available',
        };
    }
},

Secure your tools

Remember that tools execute server-side. Keep security in mind:

• Validate inputs - Don't trust user-provided data
• Use least privilege - Only grant tools the permissions they need
• Protect secrets - Store API keys in environment variables
• Rate limit - Consider adding rate limiting for expensive operations

execute: async ({ serviceName }) => {
    // Validate the service name exists in your catalog
    const validServices = await getServices();
    if (!validServices.includes(serviceName)) {
        return { error: 'Service not found in catalog' };
    }

    // Proceed with the query...
},

Complete example

Here's a complete eventcatalog.chat.js with multiple tools:

eventcatalog.chat.js

import { anthropic } from '@ai-sdk/anthropic';
import { tool } from 'ai';
import { z } from 'zod';

// Your model configuration
export default async () => {
    return anthropic('claude-haiku-4-5');
}

export const configuration = {
    temperature: 0.7,
    maxTokens: 10000,
}

// Your custom tools
export const tools = {
    getEventMetrics: tool({
        description: 'Get real-time production metrics for an event including throughput, latency, and error rates.',
        inputSchema: z.object({
            eventId: z.string(),
            timeRange: z.enum(['1h', '24h', '7d', '30d']).default('24h'),
        }),
        execute: async ({ eventId, timeRange }) => {
            // Your implementation
        },
    }),

    getServiceHealth: tool({
        description: 'Get the current health status of a service including uptime and active instances.',
        inputSchema: z.object({
            serviceName: z.string(),
        }),
        execute: async ({ serviceName }) => {
            // Your implementation
        },
    }),

    getOnCall: tool({
        description: 'Get the current on-call engineer and escalation contacts for a service.',
        inputSchema: z.object({
            serviceName: z.string(),
        }),
        execute: async ({ serviceName }) => {
            // Your implementation
        },
    }),

    getQueueDepth: tool({
        description: 'Get the current queue depth and consumer lag for an event.',
        inputSchema: z.object({
            eventId: z.string(),
            environment: z.enum(['production', 'staging', 'development']).default('production'),
        }),
        execute: async ({ eventId, environment }) => {
            // Your implementation
        },
    }),
};

Viewing available tools

Users can see all available tools (including custom ones) by clicking the wrench icon in the chat panel. Custom tools are labeled with a "Custom" badge to distinguish them from built-in tools.

What can you build?

The possibilities are endless. Here are some ideas:

• Cost tracking - "How much did the OrderCreated event cost to process last month?"
• Compliance checks - "Does the PaymentService meet our SLA requirements?"
• Deployment info - "When was InventoryService last deployed?"
• Incident history - "What incidents has NotificationService had this quarter?"
• Schema validation - "Would this schema change break any consumers?"
• Test coverage - "What's the test coverage for OrderService?"
• Documentation gaps - "Which events are missing descriptions?"

Custom tools turn EventCatalog into your organization's single pane of glass for architecture knowledge—combining static documentation with live operational data.

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