ParseHub MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ParseHub as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 ParseHub. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ParseHub?"
)
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 ParseHub MCP Server
Bring ParseHub Cloud Scraping directly into your AI workflows. Manage pre-configured web scraping targets natively and orchestrate complex headless browser automation directly from chat. Dispatch run jobs on command, query execution status limits, and extract final parsed payloads securely.
LlamaIndex agents combine ParseHub tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Project Navigation — Inspect and list configured ParseHub projects, determining start URLs, templates, and total crawler pages attached
- Execution Dispatch — Command remote servers to trigger specific headless data extraction jobs
run_projectoptionally overriding starting URLs natively - Observability Tracing — Monitor exactly where a
Runobject is (queued, initialized, running, complete) without checking the desktop app - Payload Extraction — Pull down structured arrays containing the scraped payloads securely via
get_run_datamatching explicit datasets
The ParseHub MCP Server exposes 10 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.
How to Connect ParseHub to LlamaIndex via MCP
Follow these steps to integrate the ParseHub MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from ParseHub
Why Use LlamaIndex with the ParseHub MCP Server
LlamaIndex provides unique advantages when paired with ParseHub through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ParseHub tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ParseHub tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ParseHub, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ParseHub tools were called, what data was returned, and how it influenced the final answer
ParseHub + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ParseHub MCP Server delivers measurable value.
Hybrid search: combine ParseHub real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ParseHub 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 ParseHub for fresh data
Analytical workflows: chain ParseHub queries with LlamaIndex's data connectors to build multi-source analytical reports
ParseHub MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ParseHub to LlamaIndex via MCP:
cancel_run
If the run was already scraping pages, partial data may be available. Data from already-scraped pages is preserved and can be retrieved with get_run_data. Use this to stop long-running scrapes or free up queue slots. Cancel a queued or actively running ParseHub run
delete_run
Cannot be undone. Use this to clean up old runs and free up storage quota on your account. Permanently delete a ParseHub run and its extracted data
get_last_ready_data
Ideal for dashboards or integrations that always want the freshest available data without managing individual run tokens. Instantly get the latest completed data for a ParseHub project
get_project
The project_token can be found via list_projects or in the ParseHub desktop client settings tab. Get detailed configuration of a specific ParseHub project
get_run_data
Only works when the run status is "complete" and data_ready is true. The JSON structure mirrors the template selection configuration set up in the ParseHub desktop client. Download the raw JSON data extracted from a completed ParseHub run
get_run_details
Poll this endpoint to wait for a run to complete before fetching data. Check the status of a specific ParseHub run
list_projects
Each project includes a project_token (unique identifier), title, last_run timestamp, and template configuration. Use the project_token for all subsequent run management operations. List all ParseHub web scraping projects
list_runs
Useful for auditing or finding a specific completed run to fetch data from. Get the history of all runs for a ParseHub project
run_project
Returns a run_token for tracking progress. The run enters a queue and begins processing within seconds. Use get_run to monitor and get_run_data to retrieve results once complete. Start a new ParseHub scraping run for a project
run_project_with_url
Perfect for scraping different pages with the same template (e.g., different product categories). The template extraction rules still apply unchanged — only the starting page changes. Start a ParseHub run targeting a custom URL instead of the project default
Example Prompts for ParseHub in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ParseHub immediately.
"Fetch the list of scrape projects I have on my ParseHub account."
"Start a new run for project 't9zx...' and check its status."
"Extract the finished data JSON payload from run ID 'run_k1l'."
Troubleshooting ParseHub MCP Server with LlamaIndex
Common issues when connecting ParseHub to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpParseHub + LlamaIndex FAQ
Common questions about integrating ParseHub MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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Connect ParseHub to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.