How to Use the EBI Proteins API MCP in AutoGen

Q: How do I register the EBI Proteins API MCP Server in AutoGen?

Use pip install -U autogen-extmcp to install the extension. Then, initialize your tool list using mcpservertools with your server parameters and pass them directly to the AssistantAgent constructor.

Q: How do AutoGen agents handle schema mismatches from EBI Proteins API?

The McpToolAdapter handles schema conversion automatically. Your agents receive clean, JSON-serializable payloads from tools like getcoordinates, matching the expected input schemas of your agents.

Let AutoGen agents debate variant annotations and protein features using live UniProt data.

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Connect EBI Proteins API MCP to AutoGen

Create your Vinkius account to connect EBI Proteins API 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 Proteins API with AutoGen

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Drive Multi-Agent Consensus on Genetic Variants

This MCP Server empowers your AutoGen agents to negotiate and verify genetic assertions using live data. One agent can pull clinical records using `get_variation`, while a second agent verifies the structural impact via `get_mutagenesis`. The agents debate the functional consequences of missense mutations by comparing evidence. They resolve conflicts dynamically before generating a final consensus report for clinical bioinformaticians.

Validate Experimental Proteomics in Agent Conversations

Set up a peer-review conversation where a mass-spectrometry agent challenges a sequence-annotation agent. The sequence agent proposes a target using `get_protein_features`, while the validation agent cross-checks it using `get_proteomics`. By querying `get_proteomics_ptm` and `get_antigen`, the agents reach a data-backed agreement on whether a specific peptide has been experimentally observed. This autonomous verification reduces human review times significantly.

Resolve Taxonomy Clashes with an MCP Server Agent

Deploy a dedicated taxonomy agent that uses `get_taxonomy` and `search_taxonomy` to resolve organism naming conflicts. When other agents query proteomes, the taxonomy agent validates the species lineage first. This agent matches queries against the correct NCBI taxon ID before allowing the group to call `get_proteome` or `search_proteomes`. This structured hierarchy prevents downstream agents from running mismatched analysis.

Setup guide

Set up EBI Proteins API 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 Proteins API 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 Proteins API_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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 Proteins API_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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 EMBL-EBI Proteins API. 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 EBI Proteins API MCP in AutoGen

Use `pip install -U autogen-ext[mcp]` to install the extension. Then, initialize your tool list using `mcp_server_tools` with your server parameters and pass them directly to the `AssistantAgent` constructor.

Yes, the conversation coordinator routes tasks based on agent feedback. For instance, one agent can run `search_proteins` to find a gene, and then pass the resulting accession ID to another agent to run `get_protein`.

The `McpToolAdapter` handles schema conversion automatically. Your agents receive clean, JSON-serializable payloads from tools like `get_coordinates`, matching the expected input schemas of your agents.

The integration supports both stdio and Streamable HTTP transports. This flexibility allows you to host the server locally during development or run it in a secure cloud environment for production multi-agent systems.

Your queries, including specific gene names and genomic coordinates, are processed inside a secure, ephemeral V8 sandbox. No data is cached permanently, and authorization tokens are handled securely at the connection layer.

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