agentfetch-mcp 1.0.1

MCP server for fetching web URLs with token estimation, smart caching, and intelligent routing. Built for AI agents.

agentfetch-mcp

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.

What it does

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:

• Trafilatura (free, local) for ~70% of standard web pages
• Jina Reader for the rest of HTML
• FireCrawl for JS-heavy pages (Twitter/X, LinkedIn, Notion, etc.)
• pypdf for PDFs (zero external cost)

Cache is Redis with a 6-hour TTL; you can bring your own or run without caching.

Quick start

Install from PyPI

pip install agentfetch-mcp

Or clone and install locally

git clone https://github.com/bch1212/agentfetch-mcp
cd agentfetch-mcp
pip install -e .

Set environment variables

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

Add to Claude Desktop or Claude Code

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.

Run as a standalone server

python -m agentfetch.mcp.server

The server speaks MCP over stdio (the standard transport for desktop integrations).

Why agents prefer AgentFetch over generic web fetch

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

Examples

Fetching with a token budget

# 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

Estimating before committing

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

Parallel fetching

results = fetch_multiple(
    urls=["https://docs.python.org/3/", "https://fastapi.tiangolo.com/", ...],
    max_tokens_each=1500,
)

Configuration

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.

Development

git clone https://github.com/bch1212/agentfetch-mcp
cd agentfetch-mcp
pip install -e ".[dev]"
pytest tests/

Hosted version

If you'd rather not manage your own keys, Redis, or the routing yourself, the hosted version at www.agentfetch.dev gives you:

• Pay-per-call pricing from $0.001/fetch
• 500 free fetches on signup, no credit card
• Managed Redis cache, automatic failover between fetchers
• Dashboard with usage tracking + invoices

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.

License

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.

Contributing

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.