Developer Research MCP Server

The Developer Research MCP Server bridges the gap between AI assistants and real‑world information by offering a dedicated web search service. In modern development workflows, an AI assistant often needs to pull up-to-date documentation, library references, or community discussions. This server supplies that capability through a standardized Model Context Protocol interface, enabling seamless integration with tools such as Roo Code or any MCP‑compliant client.

At its core, the server exposes a single, well‑defined tool: . When an AI agent invokes this tool, the server forwards the query to a configurable research provider—currently OpenRouter—and returns results in a consistent JSON payload. The payload includes the original query, a list of ranked results with titles, URLs, and snippets, and metadata such as the source provider. By delivering structured data instead of raw HTML or text, developers can parse and display search results programmatically, embed them into dashboards, or feed them back to the assistant for summarization.

The architecture is intentionally modular. Each research provider implements a common interface, allowing developers to swap in new search engines or databases without touching the core MCP logic. Future enhancements could support multiple providers simultaneously, weighted ranking, or provider‑specific query tuning. This extensibility is crucial for teams that rely on proprietary knowledge bases or want to combine public search with internal documentation repositories.

Key capabilities include:

• Optimized for technical content: The provider configuration prioritizes developer‑centric sources, ensuring that search results are relevant to programming questions.
• Robust error handling: Automatic retries and graceful degradation keep the service reliable even when external APIs experience hiccups.
• Consistent JSON output: A fixed schema simplifies downstream processing, enabling developers to build UI components or analytics pipelines that consume search results directly.

Typical use cases span the entire development lifecycle. A developer working in an IDE might ask the AI assistant, “What is the latest best practice for handling asynchronous errors in Node.js?” The assistant forwards that query to , receives structured results, and presents a concise answer. In continuous integration pipelines, the server can validate that documentation references remain current by periodically searching for outdated links. For research teams, it provides a lightweight, programmable gateway to aggregate information from multiple web sources without writing custom scrapers.

By integrating this MCP server into existing AI workflows, teams gain a powerful, extensible research layer that keeps assistants informed with real‑time data. Its straightforward configuration, coupled with a modular provider architecture, makes it an attractive addition to any development environment that values up‑to‑date knowledge and programmatic control over search results.