Tool Modifiers

Tool modifiers intercept and modify tool inputs and outputs using decorators.

Types

• Pre-modifiers: Modify tool inputs before execution
• Post-modifiers: Modify tool outputs after execution

Tip

Common uses:

• Reduce response size to prevent LLM context overloading
• Filter emails to unread only
• Add consistent parameters
• Transform data formats

Pre-Modifiers

Pre-modifiers modify tool inputs before execution.

Use Cases

• Enforce consistent filters or constraints
• Add security parameters or validate inputs
• Override LLM decisions with required behavior
• Set default configurations

Example: Gmail Unread Filter

from scalekit.actions.models.tool_input_output import ToolInput

# For example, we can modify the query to only fetch unread emails
# regardless of what the user asks for or what the LLM determines.
@actions.pre_modifier(tool_names=["gmail_fetch_mails"])
def gmail_pre_modifier(tool_input: ToolInput):
    tool_input['query'] = 'is:unread'
    return tool_input

This modifier:

• Intercepts all calls to gmail_fetch_mails
• Forces the query to always search for unread emails only
• Ensures consistent behavior regardless of user input or LLM interpretation

Multiple Tools Example

@actions.pre_modifier(tool_names=["gmail_fetch_mails", "gmail_search_emails"])
def email_security_modifier(tool_input: ToolInput):
    # Add security constraints to all email operations
    tool_input['include_spam'] = False
    tool_input['max_results'] = min(tool_input.get('max_results', 10), 50)
    return tool_input

Post-Modifiers

Post-modifiers modify tool outputs after execution.

Note

Response Format: Post-modifiers must always return a dictionary with a "response" key: {"response": your_data}

Use Cases

• Reduce token usage by filtering large responses
• Transform formats for LLM consumption
• Extract specific data from responses
• Standardize output structure

Example: Gmail Response Filtering

from scalekit.actions.models.tool_input_output import ToolOutput

# Sometimes, the tool output needs to be modified in a deterministic way after the tool is executed.
# For example, we can modify the output to only return the first email snippet regardless of what the tool returns.
# This is an effective way to reduce the amount of data that is returned to the LLM to save on tokens.
@actions.post_modifier(tool_names=["gmail_fetch_mails"])
def gmail_post_modifier(output: ToolOutput):
    # Only return the first email snippet
    # Should return a dict
    # Response should be a dict with a key 'response'
    for snippet in output['messages']:
        print(f"Email snippet: {snippet['snippet']}")
    return {"response": output['messages'][0]['snippet']}

This modifier:

• Processes the response from gmail_fetch_mails
• Extracts only the first email snippet instead of returning all emails
• Reduces token usage by sending minimal data to the LLM