Do I need the ParseHub Desktop tool running to use this?

No. This integration operates completely natively via ParseHub's Cloud API endpoints. You only need the desktop app to build the templates originally. All executions mapped here happen on their cloud scaling servers.

Can I provide a different Start URL when running a project?

Yes. The `run_project_with_url` command allows you to explicitly provide a `start_url` query property. This instructs the ParseHub crawler to ignore its project-saved URL and begin parsing the newly mapped domain using the same semantic template.

Is the downloaded data returned in JSON or raw HTML?

The payload fetched by `get_run_data` is exported entirely as structured, pre-parsed JSON mirroring the exact template node selections defined in your project architecture.

ParseHub MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: ParseHub
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect ParseHub through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "parsehub": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using ParseHub, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with ParseHub through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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_project optionally overriding starting URLs natively
Observability Tracing — Monitor exactly where a Run object is (queued, initialized, running, complete) without checking the desktop app
Payload Extraction — Pull down structured arrays containing the scraped payloads securely via get_run_data matching explicit datasets

The ParseHub MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain 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 LangChain via MCP

Follow these steps to integrate the ParseHub MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from ParseHub via MCP

Why Use LangChain with the ParseHub MCP Server

LangChain provides unique advantages when paired with ParseHub through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine ParseHub MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across ParseHub queries for multi-turn workflows

ParseHub + LangChain Use Cases

Practical scenarios where LangChain combined with the ParseHub MCP Server delivers measurable value.

RAG with live data: combine ParseHub tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query ParseHub, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain ParseHub tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every ParseHub tool call, measure latency, and optimize your agent's performance

ParseHub MCP Tools for LangChain (10)

These 10 tools become available when you connect ParseHub to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting ParseHub to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

ParseHub + LangChain FAQ

Common questions about integrating ParseHub MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.