Firecrawl MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Cancel Active Crawl, Get Api Usage, Get Crawl Status, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Firecrawl as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Firecrawl app connector for LlamaIndex is a standout in the Friends Mcp category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Firecrawl. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Firecrawl?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Firecrawl MCP Server
Connect your Firecrawl account to any AI agent and take full control of your web data acquisition and recursive crawling workflows through natural conversation.
LlamaIndex agents combine Firecrawl tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Web Scrape Orchestration — Scrape any single URL programmatically into high-fidelity Markdown, excluding boilerplate content like headers and footers automatically
- Recursive Crawling — Programmatically discover and scrape all subpages starting from a root URL to build comprehensive knowledge bases and RAG pipelines
- Site Mapping — Quickly identify all reachable links on a domain without full content extraction to understand website structures and hierarchies
- Visual Capture — Capture full-page screenshots of any URL directly through your agent to maintain a visual record of web data
- Usage Monitoring — Track your Firecrawl credit usage, remaining limits, and active crawl job statuses in real-time
The Firecrawl MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Firecrawl tools available for LlamaIndex
When LlamaIndex connects to Firecrawl through Vinkius, your AI agent gets direct access to every tool listed below — spanning data-extraction, markdown-conversion, rag-pipelines, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Stop an ongoing crawl
Check Firecrawl credit usage
Check the status of a crawl job
Discover all URLs on a site
Turn a single URL into clean Markdown
Returns a job ID. Recursively crawl a website
Connect Firecrawl to LlamaIndex via MCP
Follow these steps to wire Firecrawl into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Firecrawl MCP Server
LlamaIndex provides unique advantages when paired with Firecrawl through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Firecrawl tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Firecrawl tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Firecrawl, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Firecrawl tools were called, what data was returned, and how it influenced the final answer
Firecrawl + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Firecrawl MCP Server delivers measurable value.
Hybrid search: combine Firecrawl real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Firecrawl to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Firecrawl for fresh data
Analytical workflows: chain Firecrawl queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Firecrawl in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Firecrawl immediately.
"Turn 'https://stripe.com/docs/api' into clean Markdown."
"Crawl 'https://docs.firecrawl.dev' recursively with a limit of 10 pages."
"Map all internal links for 'https://github.com/vinkius'."
Troubleshooting Firecrawl MCP Server with LlamaIndex
Common issues when connecting Firecrawl to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFirecrawl + LlamaIndex FAQ
Common questions about integrating Firecrawl MCP Server with LlamaIndex.