Plainserp Offers Clean Google Results for AI Agents

Overview

A new service called Plainserp is aiming to simplify how AI agents interact with web search data by offering raw Google results in clean JSON format. Priced at just $0.30 per 1,000 queries, the platform strips away the visual clutter of modern search engine results pages to provide developers with exactly what automated systems need: titles, links, and snippets.

The service positions itself as a dedicated search API for AI agents, contrasting sharply with traditional scraping methods or official but costly enterprise APIs. By focusing exclusively on organic results, Plainserp claims to offer a cost-effective solution for applications ranging from retrieval-augmented generation to automated market research.

"A search API for AI agents. Titles, links and snippets as clean JSON, without the boxes Google wraps around them."

The Problem with Raw Search Data

Modern search engine results pages have evolved into complex interfaces designed for human users. They feature AI Overviews, sponsored ads, "People also ask" dropdowns, video packs, and knowledge panels. For an AI agent parsing this data, these elements are largely noise. They consume valuable tokens that the model must process and then discard, increasing costs without adding value to the final output.

Plainserp addresses this by filtering out the distractions. The service returns only the core organic results, preserving their original ranking position while delivering the data in a structured, machine-readable format. This approach allows language models to focus their computational resources on meaningful content rather than parsing HTML structures or ignoring promotional material.

The platform explicitly drops AI-generated summaries, ads, and shopping carousels. According to the company, this targeted approach ensures that agents receive high-signal data without the overhead of processing irrelevant page elements.

How the API Works

The service operates through a single, straightforward endpoint that integrates easily into existing agent workflows. Developers can query the API using standard parameters such as the search term, number of results, target country, and language. The system supports fetching up to 20 results per call, with the option to paginate through up to six pages, yielding a total of 120 results deep per query.

A sample response demonstrates the clean structure provided to developers. Instead of returning a raw HTML document, the API outputs a JSON object containing an array of results. Each result includes the position, title, URL, and snippet, alongside optional thumbnail images where available. The cost for such a request is minimal, calculated at $0.0003 per query, reflecting the lean infrastructure behind the service.

The platform also offers an MCP server for clients that support model context protocol, allowing AI tools to access web search capabilities without custom code integration. This lowers the barrier to entry for developers looking to add search grounding to their agents quickly.

Use Cases and Applications

While designed primarily for AI agents, the utility of Plainserp extends to various other domains that require structured web data. The company highlights several key use cases in its documentation, emphasizing flexibility and ease of integration.

1. AI Agents: Providing agents with a reliable search tool they can call iteratively to gather information and ground their responses in real-time data.
2. RAG and Grounding: Enhancing retrieval-augmented generation systems by pulling fresh, relevant content from the web rather than relying solely on static internal documents.
3. SEO Rank Tracking: Monitoring keyword positions across different countries and languages to track organic search performance over time.
4. Brand and News Monitoring: Detecting new mentions of brands, products, or competitors as they appear in search results.
5. Lead and Company Research: Automatically finding official websites and contact pages for lists of companies.
6. Market Research: Mapping the competitive landscape by analyzing which sites appear for specific industry topics.

Additionally, the service supports no-code automations through integrations with platforms like Zapier, Make, and n8n, allowing users to add search steps to existing workflows without writing code. It also serves researchers and dataset creators who need to collect search results at scale for training or evaluation purposes.

Pricing and Reliability

Plainserp employs a transparent, usage-based pricing model with no monthly subscriptions or minimum commitments. Users fund their accounts starting at $6, and each search consumes $0.0003 from their balance. The first 1,000 searches are provided free of charge, requiring no credit card information to begin testing.

A key feature of the pricing structure is that failed or blocked requests are never charged. This ensures that users only pay for successful data retrievals. The company notes that because the service is independent and not affiliated with Google, occasional outages may occur due to changes in Google's result formats. However, the policy of not charging for failures mitigates the risk for users.

In terms of performance, most API calls return within one to two and a half seconds. While the service acknowledges it may not be the fastest option on the market, it prioritizes cost-efficiency and dependability for agent-based applications. Default rate limits are set at 300 searches per minute, with options to request higher limits through the dashboard.