The Problem

While building a local autonomous developer team, I noticed my "Researcher Agent" was inefficient. It would hallucinate details about complex topics (like hardware specs) or waste time running sequential searches, creating a bottleneck in the workflow.

The Solution

I contributed a deep_research tool to the open-source Serper MCP Server. Instead of a simple keyword lookup, this tool transforms the search process:

1. Expands the Query

Automatically generates three variations of the user's prompt to cover different aspects of the request:

• Technical: Targeted at specifications and engineering details.
• Community/Social: Looks for discussions, reviews, and real-world experiences.
• Official Documentation: Prioritizes manuals, whitepapers, and official specs.

2. Parallels Execution

Fires all three queries simultaneously using Python's asyncio, slashing wait times and maximizing throughput compared to sequential execution.

3. Optimizes Context

Filters and deduplicates the results before feeding them back to the LLM. This ensures the agent gets high-signal data without blowing up the token budget, reducing noise and cost.

Impact

My local agents can now verify complex engineering specs (like "RTX 5090 vs 4090 memory bandwidth") in a single, verified step. This significantly improves the accuracy of my automated coding pipeline and reduces the rate of hallucinations for technical queries.

Tech Stack

• Python: Core implementation language.
• Model Context Protocol (MCP): The standard for connecting AI models to external tools.
• Serper API: Google Search API for retrieving web results.
• Asyncio: For concurrent execution of search queries.