How to Use the EBI InterPro MCP in LlamaIndex

Q: Can I use EBI InterPro tools to ground LlamaIndex agent responses?

Yes, tools like getentry and getpfamentry provide real-time facts directly to the agent. The agent injects these verified domain descriptions and literature counts into its context window. This process eliminates generic or hallucinated functional claims.

Q: How does the EBI InterPro MCP Server handle structural queries in LlamaIndex?

getstructure retrieves PDB structures mapped to InterPro annotations. LlamaIndex stores these structural mappings alongside your sequence data. Your agent can then query physical coordinates and sequence domains in a single step.

Q: What happens if a tool returns a null result for a novel sequence?

If getentrystructures returns no matching PDB files, your agent recognizes the gap. It can fall back to sequence-based tools like getpfamentry to suggest homologous domains. This ensures your pipeline doesn't break when analyzing uncharacterized proteins.

Index live EBI InterPro protein data directly into your LlamaIndex knowledge base.

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Connect EBI InterPro MCP to LlamaIndex

Create your Vinkius account to connect EBI InterPro to LlamaIndex 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 LlamaIndex

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Query an MCP Server to index raw protein data

`search_entries` queries the InterPro database for specific functional terms like kinase or zinc finger. The LlamaIndex MCP adapter takes the raw JSON output and indexes it directly into your vector store. This turns dynamic biological data into searchable knowledge. You query past search sessions using semantic search instead of relying on exact string matches. This setup blends live API results with your local paper repository. Your agent retrieves context from both sources to answer complex annotation questions.

Build grounded RAG pipelines for sequence analysis

`get_protein` retrieves detailed sequence data, source organism information, and domain counts for any UniProt accession. This tool supplies the grounded facts your RAG pipeline needs to prevent hallucinations. Your agent bases its summaries on verified EBI records. By linking `get_protein_entries` with your index, you cross-reference active sequences with cataloged domains. Keep it simple — the system builds an accurate profile of the protein architecture without manual literature searches.

Query taxonomic and structural distributions

`get_taxonomy` fetches detailed lineage data and entry counts for specific organisms. This tool allows your index to group structural and functional data by evolutionary branches. You can query your knowledge base for domain distribution across specific bacterial strains. The agent uses `get_entry_structures` to append physical structure data to taxonomic nodes. This combines 3D structural data with evolutionary history. Your queries yield answers that respect both sequence conservation and physical folding constraints.

Setup guide

Set up EBI InterPro MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all EBI InterPro MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to EBI InterPro tools.",
)
response = await agent.run("List recent EBI InterPro data")

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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 LlamaIndex

You use the LlamaIndex MCP tool spec to connect to your Vinkius endpoint. The tools retrieve raw biological data, which the framework converts into document nodes. These nodes are then indexed into your vector store for semantic retrieval.

Yes, tools like `get_entry` and `get_pfam_entry` provide real-time facts directly to the agent. The agent injects these verified domain descriptions and literature counts into its context window. This process eliminates generic or hallucinated functional claims.

`get_structure` retrieves PDB structures mapped to InterPro annotations. LlamaIndex stores these structural mappings alongside your sequence data. Your agent can then query physical coordinates and sequence domains in a single step.

If `get_entry_structures` returns no matching PDB files, your agent recognizes the gap. It can fall back to sequence-based tools like `get_pfam_entry` to suggest homologous domains. This ensures your pipeline doesn't break when analyzing uncharacterized proteins.

Vinkius manages the MCP Server inside a zero-trust, ephemeral sandbox. Your UniProt accessions and taxonomy IDs are processed in memory and never stored. All communication is protected by a single endpoint token, keeping your research data secure.

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