Unlock Smarter Data Retrieval: The Power of Composable Functions

In the world of software development, we've learned a powerful lesson time and time again: monolithic structures are brittle. We've broken down monolithic applications into microservices and monolithic frontends into components. So why are we still wrestling with monolithic data queries?

You know the one. It’s that 200-line SQL query or that tangled chain of API calls that does everything: it validates inputs, joins six tables, transforms data, calculates a new field, and formats the output. It’s a masterpiece of complexity, but it’s also a nightmare to debug, impossible to reuse, and terrifying to modify.

At Searches.do, we believe in a better way. Our platform helps you turn any data retrieval logic into a simple, secure API endpoint we call a Search Agent. But the real magic happens when you go a level deeper and embrace a core principle of modern software design: composition.

Enter Functions—small, reusable, and composable building blocks that allow you to construct sophisticated and maintainable Search Agents.

The Problem with Monolithic Data Retrieval

Before we dive into the solution, let's diagnose the pain. Relying on single, oversized queries or scripts to handle all your data retrieval logic leads to several chronic issues:

• Code Repetition: The same logic for validating an email format or standardizing a phone number gets copied and pasted across multiple queries and services.
• High Cognitive Load: A single query that tries to do everything is incredibly difficult for a new developer (or even the original author six months later) to understand.
• Brittleness: Changing one small part—like how a price is formatted—risks breaking the entire retrieval process in unexpected ways.
• Limited Reusability: You can't easily take just the "currency conversion" part of your giant query and use it somewhere else. The logic is hopelessly entangled.

This approach is the enemy of agility. It slows down development, increases the risk of bugs, and makes your data architecture rigid.

The Searches.do Solution: Building with Functions

In Searches.do, an Agent defines a workflow for retrieving data. Instead of packing all the logic into one step, you can compose a series of steps using Functions. Think of them not as one giant wrecking ball, but as a set of precision tools, each designed for a specific job.

A Function in Searches.do is a self-contained unit of logic that can be chained together within an Agent to perform tasks like:

1. Validation

Ensure data is clean before you even query your data source. This prevents wasted resources and provides faster, more specific error feedback.

• Example Function: isValidSKU: Checks if an input string matches the company's SKU format (e.g., PROD-XXXX-XX).
• Example Function: isEmailInAllowedDomain: Validates that a user's email belongs to a corporate domain for a B2B search.

2. Transformation

Modify, cleanse, or standardize data, either from the initial input or from the data returned by your core search.

• Example Function: standardizeAddress: Takes raw address components and formats them into a consistent structure.
• Example Function: convertToCent: Converts a dollar value like 19.99 into an integer 1999 for processing with payment APIs.

3. Formatting & Enrichment

Prepare the final payload for the consumer. This decouples your internal data structure from the API contract you expose to the world.

• Example Function: formatAsJSONAPI: Takes the raw data and structures it according to the JSON:API specification.
• Example Function: enrichWithAvatar: Takes a userId from the search result, calls a separate user profile service, and adds an avatarUrl to the final object.

A Practical Example: From Monolith to Composition

Let's imagine you're building an Agent called get-product-details.

The Monolithic Way:
You might write a single, complex SQL query with CASE statements for validation, JOINs for enrichment, and string concatenation for formatting. It's powerful but clunky.

-- A simplified example of a monolithic beast
SELECT
  p.id,
  p.name,
  -- Currency conversion and formatting is baked in
  '€' || ROUND(p.price_usd * 0.93, 2) AS price_eur,
  -- Enrichment logic is baked in
  s.name AS supplier_name
FROM products p
LEFT JOIN suppliers s ON p.supplier_id = s.id
WHERE
  -- Input validation is baked in
  p.sku = 'VALID-SKU-123' AND p.is_active = TRUE;

The Composable Searches.do Way:
You define a clear, readable workflow inside your get-product-details Agent.

1. Input: A product_sku and a currency preference.
2. Step 1 (Function): validateSKU
   • Receives the product_sku.
   • If the format is invalid, the agent stops and returns a clear error.
3. Step 2 (Core Search): findProductBySKU
   • Runs a simple, clean query: SELECT * FROM products WHERE sku = ?.
   • This step's only job is to get the raw product data.
4. Step 3 (Function): convertToUserCurrency
   • Takes the raw price and the input currency.
   • Calls an internal currency conversion service to get the correct value.
5. Step 4 (Function): formatFinalResponse
   • Assembles the final object, selecting only the necessary fields, adding the converted price, and ensuring a consistent structure.

The result is an Agent that is not only powerful but also transparent, testable, and incredibly easy to maintain.

The Benefits Are Clear

By breaking down logic into composable Functions, you gain immense advantages:

• Maintainability: Need to update your currency conversion logic? Just edit the convertToUserCurrency Function. The rest of the Agent remains untouched.
• Reusability: The validateSKU Function is gold. You can now reuse it in your update-product-stock Agent, your add-new-product Agent, and anywhere else you handle SKUs.
• Testability: Each Function can be tested in isolation, guaranteeing its behavior before it's ever placed in a complex workflow.
• Readability: Your Agent's workflow becomes a declarative list of steps. It's "Business-as-Code"—the logic is clear, explicit, and easy to understand at a glance.

Go Beyond the Monolith

Stop wrestling with brittle, oversized queries. It's time to apply the same principles of composition and reusability to your data retrieval that you already apply to the rest of your stack. By building your Search Agents with small, focused Functions, you're not just retrieving data; you're creating a robust, scalable, and maintainable data access layer.

Ready to start building smarter search and data retrieval agents? Explore Searches.do and transform your complex queries into simple, powerful Services-as-Software.