How to Use the EBI InterPro MCP in CrewAI

Deploy a specialized crew of autonomous agents to map protein functions using the EBI InterPro MCP Server for hands-off research.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect EBI InterPro MCP to CrewAI

Create your Vinkius account to connect EBI InterPro to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup EBI InterPro with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Autonomous protein research with CrewAI

Assign a dedicated agent to run `search_proteins` and `get_protein_entries` while another agent summarizes the results. CrewAI coordinates these roles, creating a research team that works without your constant input. This setup is perfect for high-throughput analysis. Your agents share memory, so the secondary agent knows exactly what the first one found in the EBI InterPro database.

Evolutionary monitoring with CrewAI

Configure an agent to watch for taxonomic shifts using `get_entry_taxonomy` and `search_taxonomy`. You can set this agent to run on a schedule, alerting you only when specific evolutionary criteria are met. Your agents act as a monitor, constantly polling the data. Because they use the EBI InterPro MCP Server, they stay updated with the latest entries without you needing to write manual scripts.

Structural verification crews in CrewAI

Build a crew where one agent identifies protein families and a second agent validates them against `get_entry_structures`. This hierarchical execution ensures your final reports are based on verified experimental data. By delegating the structural checks to a specific agent, you reduce the risk of false positives. The crew handles the heavy lifting of cross-referencing entries and structures.

Setup guide

Set up EBI InterPro 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 EBI InterPro tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent EBI InterPro 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="EBI InterPro Analyst",
        goal="Access and analyze EBI InterPro data via MCP.",
        backstory="Expert analyst with direct EBI InterPro access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent EBI InterPro 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 InterPro. 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 EBI InterPro 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 EBI InterPro MCP in CrewAI

You pass the server URL directly to the agent definition. The agents then automatically list and use the EBI InterPro tools based on their assigned roles.

Use the tool filter when initializing the MCP server. This limits your agents to only the tools you want them to have access to during their run.

The server runs in a secure, ephemeral Vinkius sandbox. Your research data is never persisted beyond the duration of your agent's task.

The server manages the connection, but you should structure your agent tasks to avoid flooding the API. Space your agent actions to maintain consistent performance.

They can use shared memory to track findings. However, each session is distinct, so ensure you configure your agent persistence if you need long-term research tracking.

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