Best Web Search APIs and MCP Tools for AI Agents in 2026

For agents that need current web pages, choose an API by what the agent must receive: source links, relevant passages, full-page text, or structured fields. Search results alone do not necessarily contain page content. A web scraping API can provide a separate fetch path when the search response is insufficient.

If an agent needs to cite a source, retain the result URL alongside any fetched text. If it needs specific fields, evaluate a documented extraction feature rather than assuming that a JSON search response contains schema-shaped page data.

Best fit: choose Context.dev when you want to search, retain source URLs, and selectively scrape pages through its web API. Choose Firecrawl when one search request should also return full-page Markdown; Context.dev supports that pattern too, but only when inline Markdown is enabled. Exa fits searches that need text, highlights, or synthesized structured output; Tavily offers snippets with optional raw content and a separate Extract API. Choose Brave Web Search for links and snippets, or its separate LLM Context endpoint for ranked passages. The table below compares the documented paths and costs.

Which Search API Fits Your Agent's Search-to-Fetch Workflow?

Compare the content returned by search with the route to a full page. "JSON" may describe the response envelope, not extracted fields from the page. Rendering is marked unspecified where the cited search and fetch documentation does not establish JavaScript execution.

API	Search result and source URL	Full-page fetch path	Rendering	Structured output	Documented pricing
Context.dev	Snippets by default; results[].url	Optional markdownOptions.enabled returns per-result Markdown in search; or pass a selected URL to Scrape	Managed rendering for scraping; see Scrape docs	Search response JSON is not schema-shaped page extraction; pricing lists JSON extraction separately	1 credit per 10 search results, plus 1 per result returned as Markdown; basic scrape 1 per page
Firecrawl	URL, title, description, and highlights	scrapeOptions.formats: ['markdown'] in search, or separate Scrape	See Scrape docs for page handling	JSON is an additional paid scrape format, not the default search-result shape	2 credits per 10 search results, rounded up, plus 1 per scraped page; JSON adds 4 per page
Exa	URL and optional text or highlights	Request content in Search or use Contents for URLs or result IDs	JavaScript rendering not specified in cited Search/Contents docs	Search outputSchema supports synthesized JSON; it is distinct from a page-body fetch	Lite $4, Auto $7, Deep $12 per 1,000 searches at up to 10 results; Contents $1 per 1,000 pages per content type
Tavily	URL and content snippets; include_raw_content can populate raw_content	Optional raw content in Search or separate Extract, which defaults to Markdown	JavaScript rendering not specified in cited Search/Extract docs	Result JSON contains content fields; it is not schema-shaped page extraction	Basic search 1 credit; advanced 2; pay-as-you-go $0.008/credit; Extract billed separately
Brave	Web Search URL, title, and snippet	No full-page fetch documented for Web Search; separate LLM Context returns ranked passages, not guaranteed full pages	JavaScript rendering not specified in cited docs	Search and LLM Context responses are JSON, not schema-shaped full-page extraction	Prepaid billing and up to $20 in free calls monthly; exact per-call rate not verified here

Fetch only the results your agent needs to read in full: a search response can be enough to choose sources, while page Markdown costs extra in both Context.dev and Firecrawl. For more focused comparisons, see Context.dev versus Firecrawl, Exa, and Tavily.

Why Does Context Engineering Matter for AI Agents?

For a search-to-fetch agent, context engineering means choosing which results warrant a full-page fetch, retaining their source URLs, and passing only relevant content into the next step. It does not require fetching every result.

Treat snippets, query-relevant passages, and full-page Markdown as different inputs. Check the endpoint's documented response before deciding whether the agent needs a second request.

What Are the Best Web Search APIs in 2026?

The API details below explain the fetch choices behind the comparison table. Both Context.dev and Firecrawl document an option to return page Markdown within a search call; neither requires that every search result be fetched in full.

1. Context.dev (/web/search)

The Context.dev web search API returns source URLs and descriptions by default. Set markdownOptions.enabled to retrieve Markdown for results in the same call, or search first and send selected results[].url values to Scrape. Pricing lists 1 credit per 10 search results, plus 1 per result returned as Markdown; a basic separate scrape costs 1 credit per page.

The endpoint accepts natural language queries as well as Google-style operators (site:, -site:, inurl:, intitle:, quoted phrases, and OR), and returns 1 to 100 results per call (10 by default). Agents can constrain retrieval with domain allowlists and blocklists, freshness windows from the last 24 hours to the last year, and per-country targeting, which keeps grounding queries precise without post-filtering (Context.dev Search API).

Because the same API key also covers structured extraction, batch scraping, monitors, and brand enrichment, teams that start with /web/search do not need to add a second vendor as their agent's scope grows. On Openbenchmarks' factual lookup board, Context.dev scored 98.0% on 300 company-news questions, second of 13 web search APIs and at the lowest cost per correct answer of the five most accurate endpoints; the details are in our Openbenchmarks write-up.

2. Firecrawl (/search)

Firecrawl Search returns URLs, titles, descriptions, and highlights. Set scrapeOptions: { formats: ['markdown'] } to include full-page Markdown in that same search request, or scrape selected URLs separately. Its documented cost is 2 credits per 10 search results, rounded up, plus 1 credit per scraped page; JSON adds 4 credits per page.

3. Exa

Exa Search can return source URLs with text or highlights; it also supports outputSchema for synthesized structured JSON. For a selected URL or result ID, Contents provides a separate full-content request. Exa prices Lite, Auto, and Deep Search from $4, $7, and $12 per 1,000 requests respectively at up to 10 results, with Contents billed separately.

4. Tavily

Tavily Search returns source URLs and content snippets; include_raw_content can populate each result's raw_content. Its separate Extract endpoint accepts URLs and defaults to Markdown output. Basic search costs 1 credit and advanced search 2; pay-as-you-go pricing is $0.008 per credit. Extract has its own credit charges.

5. Brave Search API

Brave Web Search returns source URLs, titles, and descriptions, not full page bodies. Its separate LLM Context endpoint returns ranked passages for agents, not a guaranteed full-page fetch. Brave documents prepaid billing and up to $20 in free calls each month; an exact per-call rate is not established by the cited material.

How Do You Search, Then Fetch a Selected Page?

MCP lets a compatible agent client call retrieval tools. It is a connection option, not a substitute for checking whether a tool returns snippets, passages, or full pages. For client-specific choices, see the published web search MCP server comparison for Cursor and Claude.

Context.dev MCP setup

For MCP clients, follow the Context.dev installation guide. For application code that chooses which search results to fetch, use the REST endpoints in the example below instead.

import os
import requests
 
API_URL = "https://api.context.dev/v1"
HEADERS = {"Authorization": f"Bearer {os.environ['CONTEXT_DEV_API_KEY']}"}
 
search = requests.post(
    f"{API_URL}/web/search",
    headers=HEADERS,
    json={"query": "Model Context Protocol specification", "numResults": 10},
    timeout=60,
)
search.raise_for_status()
results = search.json()["results"]
 
selected = [result for result in results if result["relevance"] == "high"][:2]
 
sources = []
for result in selected:
    page = requests.post(
        f"{API_URL}/web/scrape",
        headers=HEADERS,
        json={"url": result["url"], "formats": {"markdown": True}},
        timeout=60,
    )
    page.raise_for_status()
    markdown = page.json()["markdown"]
    if markdown["success"]:
        sources.append({"url": result["url"], "markdown": markdown["data"]})
 
for source in sources:
    print(source["url"], len(source["markdown"]))

The search call costs 1 credit for 10 results, and each selected scrape costs 1 more. Every entry in sources keeps its URL next to the fetched Markdown, so the agent can cite what it read.

What Are the Best Web Scraping Tools for Live Extraction?

When search snippets are insufficient, fetch a selected source URL and inspect the returned page content before passing it to the agent. Rendering behavior depends on the provider and endpoint; do not infer it from a JSON response or a Markdown format option alone. For dedicated scraping choices beyond this comparison, see the web scraping API overview.

Frequently Asked Questions

How can an agent search current web pages? Call a documented search endpoint with a query, then keep each result's source URL. If the agent needs more than snippets, request content through that provider's documented search option or fetch endpoint.

How do I get clean page text after searching? Fetch the selected result URL as Markdown when snippets are insufficient. Context.dev Scrape and Firecrawl Scrape document page-content paths; Tavily Extract defaults to Markdown.

Can search return structured results and source links? These APIs return result objects with source URLs, but JSON search results are not automatically extracted page fields. Exa Search separately documents outputSchema for synthesized structured output; check the extraction feature and its billing before relying on a schema.

Can one API request both search and fetch pages? Yes. Context.dev Search can include Markdown with markdownOptions.enabled; Firecrawl Search can include it with scrapeOptions.formats: ['markdown']. Both charge for the pages retrieved. If you only need a few pages, search first and fetch selected URLs.

Strategic Recommendations for Your AI Stack

Choose according to the content your agent actually consumes, and account for page-fetch charges separately from search:

1. Selective search-to-fetch: Use Context.dev Search to find source URLs, then Scrape only the pages your agent needs.
2. One-call search plus page Markdown: Compare Context.dev's inline Markdown option with Firecrawl's search scrapeOptions; both document additional page charges.
3. Search with content options: Consider Exa for text, highlights, or synthesized structured output, and Tavily for snippets with optional raw_content.
4. Links or ranked passages: Use Brave Web Search for URLs and snippets; evaluate its separate LLM Context endpoint when passages meet the agent's needs.

Start with a Search request, retain the source URLs, and fetch only the pages the agent needs. For a client-based integration rather than REST, see the Cursor and Claude MCP comparison.