Tools & Integrations - eigi.ai Documentation

Overview

eigi.ai agents become powerful when connected to your existing systems. Use Dynamic API Tools for REST integrations and MCP (Model Context Protocol) for advanced AI tool connections.

Already have your own OpenClaw, Harness, or custom OpenAI-compatible Responses API? Follow Connect Your Own Harness for the simplest setup path.

Dynamic API Tools

What Are API Tools?

API Tools let your agents interact with external services during conversations:

Check order status in your database
Book appointments in your calendar
Look up customer information in your CRM
Process payments through payment gateways
Send emails or SMS notifications

Creating an API Tool

Define the Endpoint

Specify the URL, HTTP method, and headers

Configure Parameters

Define what data the tool needs

Map Responses

Tell the agent how to use the response

Test the Tool

Validate it works before using in production

Assign to Agent

Add the tool to your agent’s capabilities

API Tool Configuration

Endpoint Settings

Setting	Description	Example
Name	Descriptive tool name	”Check Order Status”
Description	What the tool does (for LLM)	“Looks up order status by order ID”
URL	API endpoint	`https://api.example.com/orders`
Method	HTTP method	GET, POST, PUT, DELETE
Headers	Request headers	Authorization, Content-Type

Authentication

Support for multiple auth methods:

API Key

Pass API key in header or query parameter

Bearer Token

JWT or OAuth bearer tokens

Basic Auth

Username and password authentication

Custom Headers

Any custom authentication headers

Parameter Types

How Data Gets to Your API

Static Values

Fixed values that don’t change:

{
  "source": "voice_agent",
  "version": "1.0"
}

LLM Parameters

Values extracted from conversation by AI:

“Get the order ID the customer mentioned”
“Extract the appointment date and time”
“Capture the email address provided”

User Input

Direct input from the caller:

DTMF digits entered
Specific prompted responses

Contact Variables

Data from the phonebook contact:

{{customer_id}}
{{account_number}}
{{email}}

Response Handling

Processing API Responses

Configure how your agent uses the response:

// API Response
{
  "order_id": "ORD-12345",
  "status": "shipped",
  "tracking_number": "1Z999AA10123456784",
  "estimated_delivery": "2025-12-06"
}

// Agent says:
"Your order ORD-12345 has shipped! The tracking number is
1Z999AA10123456784 and it should arrive by December 6th."

Error Handling

Define fallback behavior:

Scenario	Configuration
API Timeout	”I’m having trouble looking that up. Let me try again.”
Not Found	”I couldn’t find an order with that number. Can you verify it?”
Server Error	”Our system is temporarily unavailable. Can I take a message?”

MCP (Model Context Protocol)

What is MCP?

MCP is a standard for connecting AI models to external tools and data sources:

Tool Discovery

MCP servers automatically expose available tools to your agent

Rich Capabilities

Access databases, file systems, specialized AI tools, and more

Standardized

Works with any MCP-compatible server

Real-Time

Persistent connections for fast tool execution

MCP Server Types

Type	Description	Use Case
STDIO	Standard input/output	Local tools, CLI integrations
HTTP	REST-based servers	Remote services, cloud tools

Adding MCP Servers

Configuration

Add Server

Provide server name and connection details

Configure Connection

Set up STDIO command or HTTP endpoint

Test Connection

Verify the server connects successfully

Discover Tools

See what tools the server provides

Assign to Agent

Enable the MCP server for your agent

Connection Settings

# STDIO Server Example
name: "Database Tools"
type: stdio
command: "npx"
args: ["@your-org/db-mcp-server"]
env:
  DATABASE_URL: "postgresql://..."

# HTTP Server Example
name: "Calendar Integration"
type: http
url: "https://mcp.yourservice.com"
headers:
  Authorization: "Bearer your_token"

MCP Performance

Optimized Initialization

eigi.ai optimizes MCP for voice conversations:

Connection Pooling

Reuse connections across calls to reduce latency

Smart Caching

Cache tool definitions and reduce startup time

Lazy Loading

Initialize servers only when needed

Health Monitoring

Automatic reconnection if servers disconnect

Performance Metrics

Metric	Target
Connection Time	< 500ms for cached servers
Tool Execution	< 200ms overhead
Reconnection	Automatic within 5 seconds

Tool Usage in Conversations

How Agents Use Tools

The LLM decides when to use tools based on conversation context: Customer: “What’s the status of my order 12345?” Agent (internally):

Recognizes need for order lookup
Calls check_order_status tool with order_id=“12345”
Receives response with status and tracking
Formulates natural response

Agent (to customer): “I found your order! It shipped yesterday and should arrive by Friday. Would you like the tracking number?”

Silent Tool Calls

Tools are called silently in the background. Customers only hear the agent’s natural response—never technical details about tool execution.

Multiple Tools

Combining Capabilities

Agents can use multiple tools in a single conversation:

Check customer identity (CRM lookup)
Verify account status (Billing API)
Check order history (Orders API)
Book follow-up appointment (Calendar tool)
Send confirmation email (Email tool)

Tool Chaining

Tools can be called sequentially based on conversation flow:

Customer asks about upgrading →
  → Check current plan (API) →
  → Get available upgrades (API) →
  → Apply upgrade if confirmed (API) →
  → Send confirmation (Email)

Testing Tools

Validation

Before going live, test your tools:

Direct Testing

Test API calls with sample data

Mock Responses

Simulate responses for edge cases

Conversation Testing

Full conversation tests with real tool calls

Error Simulation

Test error handling and fallbacks

Monitoring

Tool Performance

Track how your tools perform:

Metric	Description
Call Volume	How often each tool is used
Success Rate	Percentage of successful calls
Latency	Average response time
Errors	Error types and frequencies

MCP Server Status

Monitor MCP server health:

Connection status (connected/disconnected)
Last activity timestamp
Error logs
Tool availability

Best Practices

Clear Descriptions: Write tool descriptions that help the LLM understand when to use them.

Handle Errors Gracefully: Always configure fallback responses for API failures.

Test Edge Cases: Verify behavior with missing data, timeouts, and errors.

Never expose sensitive credentials in tool configurations. Use environment variables or secure credential storage.

Troubleshooting

Tool not being called

Check the tool description—LLM may not understand when to use it - Verify the tool is assigned to the agent - Test with explicit prompts mentioning the tool’s function

MCP server not connecting

Verify connection settings (URL, command, arguments) - Check server logs for errors - Ensure required environment variables are set - Test the server independently first

Slow tool responses

Optimize your API endpoints - Consider caching frequently accessed data - Use connection pooling for MCP servers - Monitor and address timeout issues

​Overview

​Dynamic API Tools

​What Are API Tools?

​Creating an API Tool

​API Tool Configuration

​Endpoint Settings

​Authentication

API Key

Bearer Token

Basic Auth

Custom Headers

​Parameter Types

​How Data Gets to Your API

​Response Handling

​Processing API Responses

​Error Handling

​MCP (Model Context Protocol)

​What is MCP?

Tool Discovery

Rich Capabilities

Standardized

Real-Time

​MCP Server Types

​Adding MCP Servers

​Configuration

​Connection Settings

​MCP Performance

​Optimized Initialization

Connection Pooling

Smart Caching

Lazy Loading

Health Monitoring

​Performance Metrics

​Tool Usage in Conversations

​How Agents Use Tools

​Silent Tool Calls

​Multiple Tools

​Combining Capabilities

​Tool Chaining

​Testing Tools

​Validation

Direct Testing

Mock Responses

Conversation Testing

Error Simulation

​Monitoring

​Tool Performance

​MCP Server Status

​Best Practices

​Troubleshooting

Overview

Dynamic API Tools

What Are API Tools?

Creating an API Tool

API Tool Configuration

Endpoint Settings

Authentication

Parameter Types

How Data Gets to Your API

Response Handling

Processing API Responses

Error Handling

MCP (Model Context Protocol)

What is MCP?

MCP Server Types

Adding MCP Servers

Configuration

Connection Settings

MCP Performance

Optimized Initialization

Performance Metrics

Tool Usage in Conversations

How Agents Use Tools

Silent Tool Calls

Multiple Tools

Combining Capabilities

Tool Chaining

Testing Tools

Validation

Monitoring

Tool Performance

MCP Server Status

Best Practices

Troubleshooting