How to Use the EBI InterPro MCP in AutoGen

Q: Can AutoGen agents debate classifications using EBI InterPro tools?

Yes, you can set up a conversation where one agent queries getpfamentry and another queries getcddentry. They compare the domain boundaries and annotations to resolve discrepancies. This debate ensures your final annotations are backed by multiple databases.

Q: How do AutoGen agents handle large taxonomic datasets from EBI InterPro?

Tools like getentryproteins can return extensive lists of matching proteins. Your agents can filter these lists using searchproteins to focus on specific model organisms. This coordination keeps the conversation context clean and prevents token overflow.

Run multi-agent debates in AutoGen to verify protein classifications with EBI InterPro.

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Connect EBI InterPro MCP to AutoGen

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

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Setup EBI InterPro with AutoGen

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Use an MCP Server to resolve annotation conflicts

`get_entry` pulls complete metadata for any InterPro family, including description, type, and database cross-references. In an AutoGen setup, one agent uses this MCP tool to propose a functional classification for an uncharacterized sequence. — just to be clear — a second agent reviews the proposal against structural data. This multi-agent debate prevents the common mistake of relying on sequence similarity alone. The agents challenge each other's assumptions using real-time database lookups. You get a consensus-driven annotation that is far more accurate than a single-pass model.

Validate evolutionary taxonomy via agent consensus

`search_taxonomy` finds taxon IDs and annotation counts for specific organism queries. Your evolutionary agent uses this tool to trace the lineage of a target protein. Meanwhile, a separate validation agent checks the results against `get_entry_taxonomy` to confirm the domain is actually conserved in that clade. The agents deliberate on the evolutionary history, identifying potential lateral gene transfers. They present a unified, verified taxonomic profile of the protein. This consensus-driven approach filters out taxonomic noise and annotation drift.

Coordinate structural and sequence analysis

`get_clan` retrieves Pfam clan accessions and member counts to group related families. An AutoGen sequence agent uses this to find broad evolutionary relationships. Simultaneously, a structural agent calls `get_entry_structures` to analyze active site conservation in 3D space. The two agents merge their findings to confirm if a protein belongs to a specific superfamily. By comparing sequence groupings with physical folds, they catch non-homologous domains that look similar but fold differently. You avoid expensive false positives in your downstream modeling.

Setup guide

Set up EBI InterPro MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes EBI InterPro tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="EBI InterPro_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent EBI InterPro data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="EBI InterPro_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent EBI InterPro data")
    print(result.messages[-1].content)

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Common questions about EBI InterPro MCP in AutoGen

Use the AutoGen MCP tool adapter to fetch the 16 protein analysis tools from your Vinkius URL. Pass the tools list to your AssistantAgent constructor. The adapter handles the schema translation so your agents can call the tools during conversations.

Yes, you can set up a conversation where one agent queries `get_pfam_entry` and another queries `get_cdd_entry`. They compare the domain boundaries and annotations to resolve discrepancies. This debate ensures your final annotations are backed by multiple databases.

Tools like `get_entry_proteins` can return extensive lists of matching proteins. Your agents can filter these lists using `search_proteins` to focus on specific model organisms. This coordination keeps the conversation context clean and prevents token overflow.

The Vinkius infrastructure handles the connection and manages API limits. If a tool call fails, your AutoGen agents can detect the error and try alternative queries. For example, they might switch from a broad protein search to a specific taxonomic node lookup.

Yes, your sequence queries, PDB IDs, and protein names are fully protected. Vinkius runs the MCP Server in an ephemeral, zero-trust sandbox. No data is stored, and all agent interactions are secured with a single endpoint token.

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