Plugin Tools Configuration

🔌 What are Plugin Tools?

Plugin tools enable your AI agent to execute individual workflow nodes as tools during conversations. Unlike workflow tools that run entire multi-step workflows, plugin tools give your agent access to specific node capabilities like:

• LLM nodes: Call other AI models for specialized tasks

• API nodes: Make HTTP requests to external services

• Data transformation nodes: Convert formats, extract structured data

• Integration nodes: Execute actions in connected services (Telegram, Google Sheets, etc.)

• Utility nodes: Perform calculations, string operations, and more

Key Differences from Workflow Tools:

• Workflow Tools: Execute complete multi-step workflows with complex logic

• Plugin Tools: Execute single workflow nodes for specific, atomic operations

• Use Plugin Tools When: You need fine-grained control over individual operations

• Use Workflow Tools When: You need to execute complex, multi-step processes

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⚙️ Plugin Tool Configuration

Each plugin tool requires the following configuration:

Required Fields

Field	Description	Example
Plugin ID	The workflow node type to use	echo, llm, openai_ask_chat_gpt
Plugin Version	Version of the node	1.0.0 (standard for all nodes)

Optional Fields

Field	Description	When to Use
Connection	Connection ID for nodes requiring authentication	Some nodes require a connection to external services. Check the specific node's documentation in the Node Library to see if a connection is required.
Input Config	Pre-configure specific input fields	When you want to fix certain parameters and hide them from the agent

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🔧 Input Configuration (Advanced)

The input_config feature allows you to pre-configure specific input fields for a plugin tool. This is useful when:

1. You want to fix certain parameters: Set a specific model, temperature, or other settings

2. Simplify the agent's decision-making: Remove fields the agent doesn't need to decide

3. Enforce consistency: Ensure certain values are always used

4. Hide complexity: Pre-configure technical details from the agent

How It Works

When you configure an input field:

1. The field is removed from the tool's schema presented to the agent

2. The agent cannot override this value

3. The pre-configured value is automatically merged during execution

4. The field becomes invisible to the AI model

Configuration Format

Each input field configuration consists of:

{
  "field_name": {
    "value": "the_actual_value",
    "description": "Optional: why this value is set"
  }
}

Example 1: Pre-configure LLM Model

Configure an LLM plugin with a fixed model and temperature:

{
  "plugin_id": "llm",
  "plugin_version": "1.0.0",
  "input_config": {
    "model": {
      "value": "google-gemini-2.0-flash-lite",
      "description": "Fixed model for cost control"
    },
    "temperature": {
      "value": 0.7,
      "description": "Balanced creativity"
    }
  }
}

Result: The agent can use the LLM tool but only needs to provide the prompt. The model and temperature are automatically set to your configured values.

Example 2: Pre-configure Message Template

Configure an echo plugin with a pre-configured greeting:

{
  "plugin_id": "echo",
  "plugin_version": "1.0.0",
  "input_config": {
    "data": {
      "value": "Hello, World!",
      "description": "Pre-configured greeting message"
    }
  }
}

Example 3: Pre-configure ChatGPT with Fixed Prompt

Configure OpenAI ChatGPT with a fixed system behavior:

{
  "plugin_id": "openai_ask_chat_gpt",
  "plugin_version": "1.0.0",
  "connection": "openai_connection_id",
  "input_config": {
    "prompt": {
      "value": "Repeat the following value: {{user_input}}"
    },
    "model": {
      "value": "google-gemini-2.0-flash-lite"
    },
    "temperature": {
      "value": 0.7
    }
  }
}

Note: You can use template variables like {{user_input}} in pre-configured values if the node supports templating.

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📋 Common Plugin Tool Configurations

1. Add LLM Plugin Tool

Give your agent access to another AI model for specialized tasks:

Configuration:

{
  "plugin_id": "llm",
  "plugin_version": "1.0.0",
  "input_config": {
    "model": {
      "value": "google-gemini-2.0-flash-lite"
    }
  }
}

Use Cases:

• Use a reasoning model for complex logic

• Use a vision model for image analysis

• Use a fast model for simple tasks

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2. Add API Call Plugin Tool

Allow your agent to make HTTP requests:

Configuration:

{
  "plugin_id": "api_call",
  "plugin_version": "1.0.0"
}

Use Cases:

• Fetch data from external APIs

• Send data to third-party services

• Integrate with custom backends

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3. Add String to JSON Plugin Tool

Parse JSON strings into structured data:

Configuration:

{
  "plugin_id": "string_to_json",
  "plugin_version": "1.0.0"
}

Use Cases:

• Parse API responses

• Convert string data to structured format

• Handle JSON in conversations

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4. Add Telegram Send Message Plugin

Send Telegram messages:

Configuration:

{
  "plugin_id": "telegram_send_message",
  "plugin_version": "1.0.0",
  "connection": "telegram_connection_id"
}

Use Cases:

• Send notifications to Telegram

• Alert users via Telegram

• Automated messaging

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5. Add OpenAI ChatGPT Plugin

Use OpenAI's ChatGPT as a specialized tool:

Configuration:

{
  "plugin_id": "openai_ask_chat_gpt",
  "plugin_version": "1.0.0",
  "connection": "openai_connection_id",
  "input_config": {
    "model": {
      "value": "gpt-4o"
    },
    "temperature": {
      "value": 0.7
    }
  }
}

Use Cases:

• Use GPT-4o for specific reasoning tasks

• Delegate complex analysis to a specialized model

• Use vision capabilities for image understanding

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🔍 Available Plugins

You can configure any of the 193+ workflow nodes as a plugin tool for your agent. Each node becomes available as a tool that your agent can execute during conversations.

Browse Available Nodes

To find the right plugin for your use case:

1. Node Library - Complete reference of all 193+ workflow nodes organized by category

2. Nodes by Category - Browse nodes by functionality (AI & LLM, Utilities, Integrations, etc.)

3. Nodes Alphabetical - Find nodes by alphabetical order

What You'll Find in Node Documentation

Each node's documentation page includes:

• Description: What the node does and its capabilities

• Input Parameters: Required and optional fields you can configure

• Connection Requirements: Whether the node needs a connection to external services

• Output Schema: What data the node returns

• Examples: Usage examples and common configurations

Finding the Plugin ID

The Plugin ID is the node's technical name shown in the node documentation. For example:

• echo - Echo node

• llm - LLM node

• openai_ask_chat_gpt - OpenAI ChatGPT node

• telegram_send_message - Telegram Send Message node

• api_call - API Call node

Tip: Use the search function in the Node Library to quickly find nodes by keyword or functionality.

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⚠️ Important Considerations

Connection Requirements

Some plugins require a connection to external services. To check if a specific node requires a connection:

1. Check Node Documentation: Navigate to the Node Library and find the specific node

2. Review Requirements: The node documentation will specify if a connection is required

3. Configure Connection: If required:

   • Navigate to: Project Settings → Connections

   • Add Connection: For the required service (OpenAI, Telegram, etc.)

   • Get Connection ID: Copy the connection ID from the connection settings

   • Configure Plugin: Set the connection field to the connection ID

Note: Each node's documentation page in the Node Library specifies its connection requirements. Always refer to the specific node documentation for accurate connection information.

Cost Considerations

Plugin tools consume credits when executed:

• LLM plugins: Cost varies by model (see Model Selection)

• API plugins: May incur external API costs

• Media plugins: Generation/processing costs apply

Best Practice: Use input_config to set cost-effective models for budget control.

Security Best Practices

1. Pre-configure sensitive parameters using input_config:

   • API endpoints that should remain fixed

   • Rate limits

   • Safety parameters

   • Model selection for consistent behavior

2. Use connections securely:

   • Store API keys and credentials in connection settings

   • Never expose credentials in input_config values

   • Reference connections by ID only

3. Monitor plugin usage:

   • Review conversation logs regularly

   • Track which plugins are being executed

   • Monitor credit consumption

   • Check for unexpected behavior

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💡 Best Practices

1. Start Simple

Begin with basic utility plugins like echo or string_to_json to understand the behavior before adding complex integrations.

2. Use Input Config Strategically

Pre-configure fields that should remain constant:

• Model selection for consistent behavior

• Temperature for predictable outputs

• API endpoints that shouldn't change

3. Combine with System Prompt

Guide your agent on when to use specific plugins:

System Prompt Example:
"You have access to the following tools:
- Use the 'llm' plugin for complex reasoning tasks
- Use the 'api_call' plugin to fetch external data
- Use the 'string_to_json' plugin when parsing JSON responses

Always explain which tool you're using and why."

4. Check Node Documentation

Before configuring a plugin:

• Review the node's documentation in the Node Library

• Check connection requirements

• Understand required vs. optional input fields

• Review examples and use cases

5. Monitor Plugin Usage

Review conversation logs to see:

• Which plugins are being used

• How often they're executed

• Success/failure rates

• Credit consumption

6. Layer Multiple Plugins

Create specialized workflows by combining plugins:

• Fetch data with api_call

• Parse with string_to_json

• Analyze with llm

• Notify with telegram_send_message

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🔄 Updating Plugin Configuration

You can modify plugin tool configuration at any time:

Add New Plugins

Simply add new plugin configurations to the plugins array.

Modify Existing Plugins

Update the configuration fields:

• Update input_config values

• Change connection IDs

• Modify plugin version if needed

Remove Plugins

Remove plugin configurations from the plugins array to disable them.

Note: Changes take effect immediately for new conversations. Existing conversations may need to be refreshed.

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📖 Related Documentation

• Workflow Tools Configuration - Configure complete workflows as tools

• MCP Tools Configuration - Configure MCP protocol integrations

• Node Library - Complete reference of all 193+ nodes

• System Prompt Configuration - Guide your agent's tool usage

• Model Selection - Choose the right AI model for your agent

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🆘 Troubleshooting

Plugin Tool Not Appearing

Possible Causes:

1. Invalid plugin_id - Verify the node type exists

2. Missing connection for nodes that require it

3. Configuration validation errors

Solution: Check the browser console for validation errors.

Plugin Execution Fails

Possible Causes:

1. Missing required input fields

2. Invalid connection credentials

3. Node-specific errors (rate limits, API failures)

Solution: Review the execution logs and verify all required inputs are provided or pre-configured.

Agent Not Using Plugin

Possible Causes:

1. System prompt doesn't guide plugin usage

2. Plugin not relevant to conversation context

3. Agent chose alternative approach

Solution: Update system prompt with clear guidance on when to use specific plugins.

Input Config Not Working

Possible Causes:

1. Field name doesn't match node schema

2. Invalid value type for the field

3. Field is required but not configured

Solution: Verify field names match the node's input schema exactly.

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For detailed node-specific configuration, see: Node Library Reference