exa-research

name	exa-research
description	Use when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources. Use over generic search when semantic relevance matters.
triggers	["research","web research","find papers","academic papers","competitor discovery","find similar sites","exa search","cited answer","scrape webpage","neural search","semantic search","look up sources"]

Exa Research

Neural web search via BlockRun. Understands meaning, not keywords. Four distinct actions for different research modes.

How to Call from MCP

As of v0.14.1 the blockrun_exa tool is path-based. Pass the endpoint name as path and the request as body:

blockrun_exa({ path: "search",       body: { query: "AI agent frameworks 2026", numResults: 10 } })
blockrun_exa({ path: "answer",       body: { query: "What is speculative decoding?" } })
blockrun_exa({ path: "contents",     body: { urls: ["https://example.com/a", "https://example.com/b"] } })
blockrun_exa({ path: "find-similar", body: { url: "https://arxiv.org/abs/2401.12345", numResults: 5 } })

Quick Decision Table

User wants...	Path	Body	Cost
Relevant URLs on a topic	search	{ query, numResults?, category? }	$0.01/call
Cited answer to a question	answer	{ query }	$0.01/call
Full text of URLs	contents	{ urls: [...] }	$0.002/URL
Pages like a given URL	find-similar	{ url, numResults? }	$0.01/call
Recent news	search + category: "news"	–	$0.01/call
Academic papers	search + category: "research paper"	–	$0.01/call
Company info	search + category: "company"	–	$0.01/call

Valid category values for search: "news", "research paper", "company", "tweet", "github", "pdf".

Python SDK Instructions

1. Initialize (Python SDK)

from blockrun_llm import setup_agent_wallet

chain = open(os.path.expanduser("~/.blockrun/.chain")).read().strip() if os.path.exists(os.path.expanduser("~/.blockrun/.chain")) else "base"
if chain == "solana":
    from blockrun_llm import setup_agent_solana_wallet
    client = setup_agent_solana_wallet()
else:
    from blockrun_llm import setup_agent_wallet
    client = setup_agent_wallet()

2. Search — Find Relevant URLs

# Basic search
result = client._request_with_payment_raw("/v1/exa/search", {
    "query": "AI agent frameworks 2025",
    "numResults": 10,
})
for r in result.get("results", []):
    print(f"{r['title']} — {r['url']}")

# Filter by category
result = client._request_with_payment_raw("/v1/exa/search", {
    "query": "transformer architecture improvements",
    "numResults": 10,
    "category": "research paper",
})

# Restrict to specific domains
result = client._request_with_payment_raw("/v1/exa/search", {
    "query": "prediction market regulation",
    "numResults": 10,
    "includeDomains": ["reuters.com", "bloomberg.com", "wsj.com"],
})

Categories: "news", "research paper", "company", "tweet", "github", "pdf"

3. Answer — Cited, Grounded Response

Use when the user asks a factual question and needs reliable sources (not Claude's training data).

result = client._request_with_payment_raw("/v1/exa/answer", {
    "query": "What is the current market cap of Polymarket?",
})
print(result.get("answer", ""))
for c in result.get("citations", []):
    print(f"  [{c.get('title')}] {c.get('url')}")

4. Contents — Fetch URL Text

Use when you have URLs and need their full text for LLM context (scraping without a browser).

urls = [
    "https://example.com/article-1",
    "https://example.com/article-2",
]
result = client._request_with_payment_raw("/v1/exa/contents", {
    "urls": urls,
})
for item in result.get("results", []):
    print(f"=== {item['url']} ===")
    print(item.get("text", "")[:500])

Up to 100 URLs per call. Returns Markdown-ready text.

5. Similar — Find Related Pages

Use to discover competitors, related research, or sites with similar content.

result = client._request_with_payment_raw("/v1/exa/find-similar", {
    "url": "https://polymarket.com",
    "numResults": 10,
})
for r in result.get("results", []):
    print(f"{r['title']} — {r['url']}")

Common Research Workflows

Competitor discovery:

# 1. Find similar companies
similar = client._request_with_payment_raw("/v1/exa/find-similar", {"url": "https://target-company.com", "numResults": 15})
urls = [r["url"] for r in similar.get("results", [])]

# 2. Fetch their about pages
contents = client._request_with_payment_raw("/v1/exa/contents", {"urls": urls[:10]})

Research synthesis:

# 1. Find papers
papers = client._request_with_payment_raw("/v1/exa/search", {
    "query": "your topic",
    "category": "research paper",
    "numResults": 20,
})

# 2. Get answer with citations
answer = client._request_with_payment_raw("/v1/exa/answer", {
    "query": "What are the key findings on your topic?",
})

When to Use Exa vs client.search()

Use blockrun_exa / _request_with_payment_raw	Use client.search()
Finding specific URLs and fetching content	Getting a summarized answer with citations
Semantic similarity search	Web + X/Twitter + news combined
Academic paper discovery	Cheaper per call for simple lookups
Domain-filtered research	Already returns a SearchResult object

Requirements

• BlockRun SDK: pip install blockrun-llm
• USDC wallet funded (see client.get_balance())
• _request_with_payment_raw is the Python SDK entry point for Exa (no dedicated method yet)