How to Use the UniProt MCP in AutoGen

Q: How do I find the best UniProt details using AutoGen?

Start by having agents debate the result of getuniprotprotein. One agent pulls the data, and another challenges it, leading to a validated conclusion about the protein.

Q: What information can I gather from UniProt using AutoGen?

The system allows agents to compare results from searchuniprot—comparing name, gene, function, and location across different queries until they reach a consensus understanding.

Q: Can AutoGen analyze all isoforms for a specific gene in UniProt?

Yes. The agents can systematically execute searchuniprotgene and then debate the implications of finding multiple functional annotations across those different protein versions.

Q: Does the MCP Server support searching by common names with AutoGen?

The searchuniprot tool handles searches using common identifiers like 'BRCA1' or 'spike protein'. Agents can then compare these results to find patterns across multiple queries.

Achieve consensus on protein function using AutoGen.

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Connect UniProt MCP to AutoGen

Create your Vinkius account to connect UniProt 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 UniProt with AutoGen

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Debate search results across multiple agents.

Use `search_uniprot` to gather initial data points. You can set up a multi-agent debate where one agent searches by 'p53' and another checks function via keywords. The system converges on the most accurate summary. This consensus-driven approach means no single search result stands alone; the answer is built through group deliberation.

Verify protein details with multiple agents.

Give one agent the task of running `get_uniprot_protein` for an ID. A second, critical-thinking agent then reviews that output against general knowledge to flag missing context or potential inconsistencies. This multi-step process ensures the final conclusion about the protein's details is thoroughly vetted.

Analyze isoforms systematically.

When you need to understand all variations of a gene, use `search_uniprot_gene`. You can assign agents roles: one compiles the isoform list, and another analyzes potential functional overlaps between those different protein versions. This is ideal for complex biological problems where multiple viewpoints are needed to reach an answer.

Setup guide

Set up UniProt 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 UniProt 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="UniProt_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent UniProt 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="UniProt_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by UniProt. 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.

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Common questions about UniProt MCP in AutoGen

Start by having agents debate the result of `get_uniprot_protein`. One agent pulls the data, and another challenges it, leading to a validated conclusion about the protein.

The system allows agents to compare results from `search_uniprot`—comparing name, gene, function, and location across different queries until they reach a consensus understanding.

Yes. The agents can systematically execute `search_uniprot_gene` and then debate the implications of finding multiple functional annotations across those different protein versions.

The `search_uniprot` tool handles searches using common identifiers like 'BRCA1' or 'spike protein'. Agents can then compare these results to find patterns across multiple queries.

This server only provides public biological research data—protein sequences and functional annotations. No private user identifiers are involved, so your agents don't need to worry about PII.

Use it with your favorite AI tools

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