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 CrewAI 10 tools — connect in under 2 minutes

Name: ParseHub
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to ParseHub through the Vinkius — pass the Edge URL in the `mcps` parameter and every ParseHub tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="ParseHub Specialist",
    goal="Help users interact with ParseHub effectively",
    backstory=(
        "You are an expert at leveraging ParseHub tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in ParseHub "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, ParseHub becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call ParseHub tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from ParseHub

Why Use CrewAI with the ParseHub MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ParseHub through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

ParseHub + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries ParseHub for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries ParseHub, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain ParseHub tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries ParseHub against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

ParseHub MCP Tools for CrewAI (10)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

ParseHub + CrewAI FAQ

Common questions about integrating ParseHub MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.