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Token-budgeted web fetch for AI agents — auto-routes Jina, FireCrawl, Trafilatura, PDF.
Token-budgeted web fetch for AI agents — auto-routes Jina, FireCrawl, Trafilatura, PDF.
AgentFetch is a well-structured MCP server for web fetching with appropriate security controls. The codebase includes SSRF protection, careful credential handling via environment variables, and proper input validation. No critical vulnerabilities were identified. Minor findings relate to logging practices and error handling edge cases that do not impact the core security posture. Supply chain analysis found 6 known vulnerabilities in dependencies (1 critical, 3 high severity). Package verification found 1 issue.
7 files analyzed · 12 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: JINA_API_KEY
Environment variable: FIRECRAWL_API_KEY
Environment variable: REDIS_URL
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-bch1212-agentfetch": {
"env": {
"REDIS_URL": "your-redis-url-here",
"JINA_API_KEY": "your-jina-api-key-here",
"FIRECRAWL_API_KEY": "your-firecrawl-api-key-here"
},
"args": [
"agentfetch-mcp"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Web intelligence for AI agents — an MCP server that fetches URLs with token estimation, smart caching, and intelligent routing built in.
AgentFetch sits between your agent and the open web. Instead of integrating Jina, FireCrawl, pypdf, and your own caching layer separately, agents call one MCP tool and AgentFetch handles routing, caching, token budgeting, and clean Markdown extraction automatically.
This repository contains the open-source MCP server. For the hosted API + dashboard + billing, see www.agentfetch.dev.
| Tool | What it's for |
|---|---|
fetch_url | Fetch a URL → clean Markdown + metadata + token count + cache info |
estimate_tokens | Get a token count before fetching, so agents don't blow context windows on huge pages |
fetch_multiple | Fetch up to 20 URLs concurrently |
search_and_fetch | Web search + fetch top N results in one round-trip |
Under the hood, AgentFetch routes URLs to the cheapest effective fetcher:
Cache is Redis with a 6-hour TTL; you can bring your own or run without caching.
pip install agentfetch-mcp
git clone https://github.com/bch1212/agentfetch-mcp
cd agentfetch-mcp
pip install -e .
Get a free Jina Reader key at jina.ai (1M tokens/mo free tier). FireCrawl is optional but recommended for JS-heavy pages.
export JINA_API_KEY=jina_xxx
export FIRECRAWL_API_KEY=fc-xxx # optional
export REDIS_URL=redis://localhost:6379 # optional
Edit your MCP config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, or run claude mcp add in Claude Code):
{
"mcpServers": {
"agentfetch": {
"command": "python",
"args": ["-m", "agentfetch.mcp.server"],
"env": {
"JINA_API_KEY": "jina_xxx",
"FIRECRAWL_API_KEY": "fc-xxx"
}
}
}
}
Restart Claude. The four tools (fetch_url, estimate_tokens, fetch_multiple, search_and_fetch) appear automatically.
python -m agentfetch.mcp.server
The server speaks MCP over stdio (the standard transport for desktop integrations).
| Feature | AgentFetch | Generic web_fetch |
|---|---|---|
| Token estimation before fetching | ✓ | ✗ |
| Smart cache (6h TTL) | ✓ | ✗ |
| Auto-routing by URL type | ✓ | ✗ |
| JS-rendered page handling | ✓ (via FireCrawl) | partial |
| PDF extraction | ✓ | ✗ |
| Truncation to fit context budget | ✓ | manual |
# Inside any MCP-aware agent (Claude Desktop, Claude Code, etc.)
result = fetch_url(
url="https://news.ycombinator.com",
max_tokens=2000, # cap response size
use_cache=True, # serve from cache if <6h old
)
# result.markdown → clean Markdown, ≤2000 tokens
# result.metadata → title, author, word_count, language
# result.cache.hit → True if served from cache
# result.fetch_info → which fetcher ran, cost, duration
estimate = estimate_tokens(url="https://very-long-article.com")
if estimate.estimated_tokens and estimate.estimated_tokens < 5000:
result = fetch_url(url="https://very-long-article.com")
else:
# too big — skip or summarize via search_and_fetch with max_tokens_each
pass
results = fetch_multiple(
urls=["https://docs.python.org/3/", "https://fastapi.tiangolo.com/", ...],
max_tokens_each=1500,
)
| Env var | Required | Default | Notes |
|---|---|---|---|
JINA_API_KEY | Recommended | — | Free tier covers ~1M tokens/mo. Without it, only Trafilatura works (still useful for ~70% of pages). |
FIRECRAWL_API_KEY | Optional | — | Needed for JS-heavy domains (Twitter, LinkedIn, Notion). 500 free credits on signup. |
REDIS_URL | Optional | — | Without Redis, fetches run uncached. |
CACHE_TTL_SECONDS | Optional | 21600 (6h) | Cache TTL for fetch results. |
git clone https://github.com/bch1212/agentfetch-mcp
cd agentfetch-mcp
pip install -e ".[dev]"
pytest tests/
If you'd rather not manage your own keys, Redis, or the routing yourself, the hosted version at www.agentfetch.dev gives you:
The hosted API is a drop-in REST equivalent — same response shapes, same routing logic. You can run the OSS MCP locally and the hosted API in parallel, or migrate between them at any time.
MIT — see LICENSE.
The MCP server in this repo is open source. The hosted product, billing, and ops infrastructure live in a separate (private) repo.
PRs welcome. If you're adding a new fetcher (e.g., Bright Data, ScrapingBee, etc.), please match the FetchResult interface in agentfetch/core/fetchers/__init__.py and add the cost to the routing logic.
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