AI agents need search APIs that go beyond returning HTML. In an agentic stack, the search tool must return structured data the LLM can reason over, integrate via tool-calling protocols (MCP, function calling), handle multiple data sources through a single tool surface, and stay within token budgets. We ranked APIs specifically for agent use cases.
Top Pick
Scavio wins for agentic stacks with native MCP support, structured JSON across 5 platforms, and credit-based pricing that scales with agent query volume.
Full Ranking
#1Our Pick
Scavio
Agents needing multi-platform structured search via MCP
Pros
- Native MCP server (mcp.scavio.dev/mcp)
- 5 platforms under one tool surface
- Structured JSON (no HTML parsing needed)
- Token-efficient responses
- LangChain package (langchain-scavio)
Cons
- No answer generation (search results only)
- Requires post-processing for synthesis
#2
Tavily
Agents that want pre-synthesized answers
Pros
- AI-generated answer included in response
- LangChain TavilySearchResults tool
- Good context window efficiency
- Search + answer in one call
Cons
- Single platform (web only)
- No product or video data
- Higher cost at scale
#3
Perplexity Sonar
Agents needing cited, synthesized answers
Pros
- Citation-backed answers
- Multiple model tiers
- Good for research agents
- Live web grounding
Cons
- Per-token + per-request pricing is complex
- No structured product data
- Single source type
- Expensive at scale
#4
Exa
Research agents needing semantic search
Pros
- Neural/semantic search (not just keyword)
- Content extraction included
- Finds similar content well
- Good for academic research
Cons
- No e-commerce data
- No video search
- $7/1K with contents is expensive
- No MCP server
#5
Serper
High-volume agents on budget (Google only)
Pros
- Cheapest at scale
- 2,500 free/month
- Fast responses
- Simple integration
Cons
- Google only
- No MCP support
- No AI Overview data
- Less structured than competitors
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| MCP Support | Native (hosted) | Community | No |
| Platforms | 5 (Google, Reddit, YouTube, Amazon, Walmart) | 1 (web) | 1 (web) |
| Answer Generation | No (structured data) | Yes (AI answer) | Yes (cited answer) |
| LangChain Integration | langchain-scavio (PyPI) | TavilySearchResults | Custom wrapper |
| Token Efficiency | High (structured JSON) | High (pre-summarized) | Medium (full responses) |
| Multi-agent Friendly | Yes (one key, shared credits) | Yes | Complex (token billing) |
Why Scavio Wins
- Native MCP server means agents discover search tools at runtime. No hardcoded tool definitions needed.
- One API key covers 5 platforms. An agent researching a product can search Google for reviews, Amazon for pricing, Reddit for sentiment, and YouTube for demos without switching providers.
- Credit-based pricing scales cleanly with agent volume. One credit per query regardless of platform or complexity.
- Structured JSON responses are token-efficient. No HTML parsing means agents spend tokens on reasoning, not on extracting data from markup.
- LangChain integration (langchain-scavio) provides typed tool classes. CrewAI and n8n integrations also available.