Search/Retrieval MCP Servers & Skills
Real-time web search and retrieval so an agent can ground its answers in current information.
What is a search/retrieval MCP server?
Search and retrieval MCP servers connect an agent to information outside its training data — real-time web search, or retrieval-augmented generation (RAG) over a specific document set — so it can ground an answer in something current and citable instead of guessing. The common thread across all five is that the agent receives back real, current text — not a static snapshot from training data — which it can then reason over, summarize, or cite, the same way a person would after actually reading a search result or a scraped page.
Why give your agent search/retrieval access?
A model's training data has a cutoff date and no visibility into anything private (your documents, your internal wiki). Search/retrieval MCP servers close that gap two ways: web search servers give an agent live access to what's on the internet right now, while RAG-style servers give it access to a specific corpus you control, with citations back to the source. This is the difference between an agent that says "I believe the current version is X" from stale training data and one that actually checks and cites a source published this week. For a RAG server pointed at your own documentation specifically, it also means answers grounded in what your team actually wrote, not a generic web result that happens to be topically related.
What to check before you install one
For web search, check which underlying search API a server wraps (Brave, Bing, Google) since you'll typically need your own API key for that provider, and consider whether that provider's result quality and rate limits fit your use case. For RAG specifically, look at how the server chunks and indexes documents, and whether it returns source citations you can verify — an answer without a traceable source is exactly the failure mode this category exists to prevent. Rate limits are worth checking too — a free-tier API key that allows a handful of requests per minute will bottleneck an agent that wants to run several searches in a row as part of one task, so match the provider's tier to how heavily you expect to use it. Usage tends to grow here as well — a project that starts with occasional lookups often ends up making search a core part of the agent's workflow, at which point it's worth revisiting whether the original provider's rate limits and pricing tier still make sense.
Which of the 5 listed search/retrieval servers should you use?
These five split into two real groups. Tavily MCP and Exa MCP Server are both search-first — Tavily focuses on extracted, LLM-ready answers with citations, while Exa is built around neural search tuned specifically for retrieval relevance rather than traditional keyword ranking; both are remote, hosted servers with straightforward API-key setup. Firecrawl MCP Server is different: it's for turning a specific URL you already have into clean markdown, not for discovering new pages, the right tool when you know exactly what page needs scraping rather than searching broadly. Apify MCP Server is the widest net — it connects to thousands of pre-built scrapers in the Apify Store, useful when your target is a specific site type (social media, maps, e-commerce) that already has a purpose-built scraper rather than a general search or single-page fetch. Brave Search MCP Server is worth choosing specifically if you want an independent search index not built on Google or Bing's infrastructure. If you just need general "search the web and summarize" behavior, start with Tavily or Exa; reach for Firecrawl, Apify, or Brave for their more specific strengths. Budget is a real factor too — if you're experimenting rather than running production traffic, start with whichever provider's free tier best matches your query volume, and only commit to a paid plan once you know which one's result quality actually fits your use case.