How to Use the EBI Proteins API MCP in LangChain

Q: How do I configure the EBI Proteins API MCP Server inside a LangChain agent?

Install the adapter using pip install langchain-mcp-adapters langgraph and initialize the client. You pass the server's HTTP endpoint to MultiServerMCPClient, call gettools(), and feed those tools directly into your agent constructor.

Q: Does this EBI Proteins API integration support LangChain state management via LangGraph?

Absolutely. You store the results of getantigen or getproteomics in your graph's state, allowing subsequent nodes to access verified peptide targets without re-querying the API.

Run multi-step bioinformatic chains in LangChain with real-time UniProt data dynamically resolved at each link.

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Connect EBI Proteins API MCP to LangChain

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

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Claude

Perplexity

Build Multi-Step Protein Annotation Chains

This MCP Server exposes 16 tools to your LangChain agent, starting with `get_protein` to fetch full UniProt entries by accession. Your agent takes the output of this initial lookup and feeds it directly into downstream analysis steps without manual data formatting. You build reasoning loops where the agent inspects the sequence, decides to run `get_protein_features` to locate active sites, and then triggers `get_mutagenesis` to assess functional impacts. Each step uses real-time API responses rather than stale local database dumps.

Trace MCP Server Tool Calls in LangSmith

Track every single data payload sent to the EBI Proteins API using native LangSmith integration. You see the exact input parameters passed to `get_variation` and the raw JSON returned, making it easy to debug failed clinical lookups. Latency, token usage, and tool execution order are fully visible in your trace timeline. When your agent chains `get_taxonomy` with `get_proteome` to verify species-specific proteomes, you know exactly which call bottlenecked the pipeline.

Correlate Genomic Coordinates with Protein Features

Connect genetic variants to spatial protein structures by combining multiple tools in a single ReAct loop. Your agent queries genomic positions using `get_coordinates` and maps those coordinates back to functional domains. The agent then calls `get_proteomics_ptm` to verify if those coordinates overlap with known post-translational modifications. This workflow replaces slow, manual flatfile parsing with dynamic, API-driven discovery.

Setup guide

Set up EBI Proteins API 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 Proteins API 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-proteins-api-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 Proteins API 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 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 LangChain

Install the adapter using `pip install langchain-mcp-adapters langgraph` and initialize the client. You pass the server's HTTP endpoint to `MultiServerMCPClient`, call `get_tools()`, and feed those tools directly into your agent constructor.

Yes, you manage rate limits by implementing custom retry logic or caching wrappers around the LangChain tool execution layer. If a tool like `search_proteins` returns a 429 status code, your chain pauses and retries based on your backoff policy.

Absolutely. You store the results of `get_antigen` or `get_proteomics` in your graph's state, allowing subsequent nodes to access verified peptide targets without re-querying the API.

Yes, you can run a hybrid pipeline where the agent searches local medical literature vectors and then uses `get_variation` to check the clinical significance of a specific mutation found in the text.

Your client sends queries directly to the MCP Server hosted in a secure V8 isolate. Only the target UniProt accession IDs or taxon IDs are transmitted to the external EBI endpoint; no proprietary pipeline code or local database schemas ever leave your infrastructure.

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