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

bash

pip install agentfetch-mcp

Or clone and install locally

bash

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.

bash

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):

json

{
  "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

bash

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

python

# 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

python

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

python

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

bash

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.