TL;DR
AI agent builders need three things: real-time web data, a memory layer, and reliable LLM calls. Tavily and Perplexity give the agent eyes on the web; Firecrawl handles deep site extraction; Pinecone or a vector-enabled Postgres gives it memory; Redis is the fast cache. Install these and your agent is production-grade on day one.
The MCP stack every AI agent builder should install
A developer building AI agents, chatbots, or autonomous workflows. Needs search, scraping, vector storage, and LLM orchestration — all as tools the agent can call.
What hurts today
- 1Web search APIs (Serper, Bing) have inconsistent results and no LLM-tuned ranking
- 2Scraping is fragile — one site layout change breaks your whole pipeline
- 3Vector DB setup (Pinecone, Weaviate, pgvector) eats a full day before you get to the agent logic
- 4Testing the agent loop means running 100s of LLM calls — no great way to cache or replay them
- 5Debugging agent hallucinations is painful without tool-level observability
Recommended MCPs (6)
Tavily
View MCPPurpose-built search for agents — returns summarised, LLM-tuned results instead of raw SERP junk. 10x faster iteration when building a research agent.
Perplexity
View MCPWhen you need actual answers with citations, not links. Perplexity MCP gives your agent a 'senior researcher' tool for deep questions.
Firecrawl
View MCPThe best open-source scraper for agent use. Markdown output, structured extraction, and crawling — all as MCP tools. Solves the 'agent needs to read a PDF or site' problem.
Supabase
View MCPPostgres with pgvector enabled — perfect memory layer for agents without the Pinecone bill. One MCP covers auth, storage, and vector search.
Redis
View MCPTool result cache, rate limit state, and ephemeral agent memory. Redis MCP means your agent can cache a Tavily search for 24h in one line.
GitHub
View MCPAgents that build code need to read repos, open PRs, and inspect issues. GitHub MCP is table stakes for any coding agent.
A real workflow
Build a research agent that monitors 50 competitor blogs. Firecrawl crawls each blog weekly, the content is embedded into Supabase pgvector, Tavily searches it semantically on user questions, Redis caches the last 100 queries to save on API bill. Your agent prompt is 20 lines; the MCPs do the heavy lifting. What used to be a 2-week infra project becomes a one-day build.
Time ROI
Typical agent dev saves 2–3 weeks on initial infra (search + scrape + vector + cache). Ongoing, ~8h/week on observability, cache management, and prompt iteration.
Recommended recipes for this role
Search Results Indexing
Run Tavily searches on scheduled topics and index the results in Supabase for trend analysis and content research.
Web Scraping to Database
Schedule a Firecrawl scrape of any website and store the structured results directly in a Supabase table for analysis.
Research Automation
Paste a research topic in Notion and an agent uses Perplexity to gather sources, summarize findings, and structure them.
Cache Invalidation Pipeline
When a Supabase row changes, the corresponding Redis cache key is automatically invalidated to keep your API fresh.
Knowledge Graph from Code
Parse your GitHub repos and build a Neo4j knowledge graph of files, functions, imports, and authors for code intelligence.
Frequently asked questions
Why use MCPs instead of direct API calls in my agent code?
MCPs are a standard protocol — swap Tavily for Exa without changing agent code, just swap the MCP. Also, MCPs are discoverable by the LLM: the agent introspects available tools and picks the right one, instead of you hardcoding which API to call when.
Can I run MCP servers from my own agent, not just Claude Code?
Yes. The MCP SDK (Python/TS) lets you connect to any MCP server from your own orchestration code. LangChain, LangGraph, and OpenAI Agents SDK all have MCP integrations now.
What's the fastest way to add memory to my agent?
Supabase + pgvector. One MCP install, one SQL migration to enable the extension, and you have a vector DB. Pinecone is faster at scale, but for <1M embeddings Supabase is dramatically cheaper.
Does Tavily or Perplexity hallucinate less?
Tavily returns real URLs + snippets, so hallucination risk is on your agent's summarisation. Perplexity returns pre-summarised answers with citations — lower hallucination on the answer, but you trust Perplexity's summary pipeline. Use Tavily for breadth, Perplexity for depth.
Can I build a multi-agent system with MCPs?
Yes — each agent gets its own MCP client with a scoped tool set. For example: a 'researcher' agent gets Tavily+Firecrawl, a 'writer' agent gets Notion+Linear. They coordinate via a shared MCP (Redis or a message queue MCP).
Other use cases
MCPs for SaaS Founders
A technical founder (0–10 employees) building a B2B SaaS who ships code, handles billing, writes marketing, and answers support — all in the same day.
MCPs for Solopreneurs & Indie Hackers
An indie hacker with a Twitter audience, a newsletter, 1–3 shipped products, and zero employees. Ships daily, markets constantly, avoids meetings.
MCPs for DevOps Engineers
A DevOps / SRE / platform engineer running Kubernetes, CI/CD, observability, and on-call rotations. Lives in a terminal.
Start with this MCP stack
Install the full stack in one command, or cherry-pick the MCPs you need.
Browse all MCPs