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serverless_function

Run custom Python or Node code inside workflows to handle complex logic and data transformations.

Instructions

Execute serverless code directly within workflows for complex operations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageYesSelect the language for the serverless function, such as 'Python' or 'Node'.python
serverless_codeYesEnter the serverless function code here, using Python or Node syntax to perform specific tasks.import json def lambda_handler(event, context): agent_context = {} # Handle POST requests if event and 'body' in event and event['body']: try: event_json = json.loads(event['body']) agent_context = event_json.get('context', event_json) except json.JSONDecodeError: # Handle case where body isn't valid JSON agent_context = {"error": "Invalid JSON in request body"} # Handle GET requests elif event and 'queryStringParameters' in event and event['queryStringParameters']: agent_context = event['queryStringParameters'] # Handle API Gateway format for both elif event and 'pathParameters' in event and event['pathParameters']: agent_context = event['pathParameters'] # Prepare response body = { "message": "Go Agent.AI Serverless Python Functions v1.0! Your function executed successfully!", "input": event, "context": agent_context } # Include CORS headers for browser access headers = { "Content-Type": "application/json", "Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "Content-Type", "Access-Control-Allow-Methods": "OPTIONS,POST,GET" } response = { "statusCode": 200, "headers": headers, "body": json.dumps(body) } return response
output_variable_nameYesProvide a variable name to store the result of the serverless function, like 'function_result' or 'api_response'.
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It only says 'execute serverless code' but omits critical details like execution environment, timeouts, security implications, or error handling. This is a significant gap for a tool that runs arbitrary code.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that communicates the core purpose efficiently. However, it sacrifices necessary detail for brevity, which slightly lowers the score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool that executes user-provided code, the description is severely incomplete. It omits security warnings, execution constraints, output details, and integration context. No output schema or annotations exist to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions for language, code, and output variable. The description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes serverless code within workflows, but it does not differentiate from many siblings like 'rest_call' or 'invoke_agent' that also perform complex operations. The verb 'execute' and resource 'code' are specific, but the scope is vague.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'invoke_llm' or 'rest_call'. The description lacks context for appropriate usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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