How to Use the EBI InterPro MCP in LangChain

Q: How does the server handle database cross-references?

listentrydatabases returns the live count of entries across all member databases in the InterPro consortium. Tools like getclan and getpfamentry let you drill down into specific Pfam groupings. This gives you direct access to the underlying database structure.

Run multi-step protein annotation chains in LangChain with real-time EBI InterPro data.

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Connect EBI InterPro MCP to LangChain

Create your Vinkius account to connect EBI InterPro to LangChain 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 LangChain

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Build multi-step protein analysis chains in LangChain

`search_proteins` initiates the discovery pipeline by finding UniProt accessions matching your target keyword. The EBI InterPro MCP Server exposes this sequence search directly to your workflow. Your LangChain agent receives this list and feeds the accessions directly into `get_protein_entries` to extract matching domain signatures. This removes the manual copy-paste work from sequence classification. The output from the domain lookup flows directly into `get_entry_structures` to grab 3D coordinates. LangChain manages this whole sequence as a single observable chain. Why this matters: you see every tool call, latency metric, and raw payload in LangSmith without writing custom glue code.

Map evolutionary conservation across species

`get_entry_taxonomy` queries the EBI database to map out exactly which organisms express a specific protein family. This tool gives your agent the raw taxonomic distribution data needed to analyze evolutionary conservation. You don't have to guess where a gene family diverges. Combine this with `get_proteome` to compare whole-genome domain coverage statistics across different strains. Your agent evaluates the taxonomy nodes and builds a clear phylogenetic distribution map. You get clean, structured data for your evolutionary biology pipelines.

Resolve functional domains and structural data

`get_structure` pulls specific PDB coordinates and maps them directly to known InterPro annotations. This tool exposes the precise physical locations of active sites and domain boundaries. Your agent uses this spatial data to verify if a predicted domain actually forms a stable 3D fold. By calling `get_pfam_entry` and `get_cdd_entry` in parallel, the agent cross-references multiple source databases. This multi-database validation cuts down on annotation drift. You catch false positives before committing wet-lab resources to a dud target.

Setup guide

Set up EBI InterPro MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes EBI InterPro tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "ebi-interpro-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent EBI InterPro transactions"
    })
    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 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.

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Common questions about EBI InterPro MCP in LangChain

Install the LangChain MCP adapter package and initialize the client with your Vinkius endpoint. You call the get tools method to fetch the 16 protein analysis tools. Pass these tools directly into your agent constructor to start running sequence queries.

Yes, that is where the framework excels. You build chains where a sequence search feeds into taxonomic validation, which then triggers structural lookups. The agent decides the logical path based on the intermediate outputs of each tool.

Every single tool call, from fetching Pfam domains to querying CDD records, gets logged in LangSmith. You track exact token usage, execution latency, and raw JSON payloads. This visibility makes debugging complex biological workflows straightforward.

`list_entry_databases` returns the live count of entries across all member databases in the InterPro consortium. Tools like `get_clan` and `get_pfam_entry` let you drill down into specific Pfam groupings. This gives you direct access to the underlying database structure.

Your protein sequences and accession numbers are highly secure. Vinkius runs this MCP Server inside a secure V8 isolate sandbox that destroys the runtime environment after execution. Your requests go through a single secure token, keeping your proprietary sequence data private.

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