How to Use the Olostep MCP in CrewAI

Q: Can a CrewAI team share Olostep usage data?

Yes, your monitor agent can call getusage periodically to ensure the entire crew stays within your predefined API limits. It acts as an automated budget controller.

Q: Can CrewAI use Olostep for multi-step scraping?

Absolutely. One agent can use scrapeurl to find links, and another can follow them. You can chain these actions together in a sequential crew process.

Q: What is the best way to filter Olostep tools in CrewAI?

Use the toolfilter option in your MCP configuration to only expose relevant tools to specific agents. For example, give the Researcher only scrapeurl and keep administrative tools like listagents for the Moderator.

Deploy specialized scraping crews using Olostep and the CrewAI framework.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Olostep MCP to CrewAI

Create your Vinkius account to connect Olostep to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Olostep with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Specialized research crews with Olostep

Assign the `scrape_url` tool to a dedicated Researcher agent within your CrewAI team. This agent handles the heavy lifting of parsing complex sites while other agents focus on analysis and synthesis. This role-based approach prevents bottlenecking. Your agents work in parallel, with the researcher providing the raw data that the analyst needs to build reports.

Manage batch operations in CrewAI

Use `create_batch` to distribute large research tasks among your agents. A Moderator agent can manage the lifecycle of these batches, checking `get_batch` periodically to report progress to the rest of the crew. This keeps your crew organized and prevents them from waiting idly for individual scrapes. It's the most efficient way to handle large-scale data gathering missions.

Verify connectivity for CrewAI missions

Run `check_olostep_status` at the start of your crew's execution to ensure all tools are ready for use. If the connection fails, the crew can abort the mission or alert you immediately. It's a small but critical step that saves time and debugging effort. You'll know immediately if your agents have the access they need to perform their jobs.

Setup guide

Set up Olostep MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Olostep tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Olostep Analyst",
    goal="Access and analyze Olostep data via MCP.",
    backstory="Expert analyst with direct Olostep access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Olostep transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Olostep Analyst",
        goal="Access and analyze Olostep data via MCP.",
        backstory="Expert analyst with direct Olostep access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Olostep transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Olostep. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Olostep now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Olostep MCP in CrewAI

Pass the Vinkius endpoint URL directly into your agent's MCP list. This allows the agents to automatically discover and use tools like `get_agent`.

Yes, your monitor agent can call `get_usage` periodically to ensure the entire crew stays within your predefined API limits. It acts as an automated budget controller.

The server uses a zero-trust architecture where every scrape is isolated. Your target URLs are never cached or stored, ensuring your research remains private to your team.

Absolutely. One agent can use `scrape_url` to find links, and another can follow them. You can chain these actions together in a sequential crew process.

Use the tool_filter option in your MCP configuration to only expose relevant tools to specific agents. For example, give the Researcher only `scrape_url` and keep administrative tools like `list_agents` for the Moderator.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript