EBI InterPro MCP Server for LlamaIndexGive LlamaIndex instant access to 16 tools to Get Cdd Entry, Get Clan, Get Entry, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add EBI InterPro as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The EBI InterPro MCP Server for LlamaIndex is a standout in the The Unthinkable category — giving your AI agent 16 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to EBI InterPro. "
"You have 16 tools available."
),
)
response = await agent.run(
"What tools are available in EBI InterPro?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About EBI InterPro MCP Server
Connect to the InterPro API and access the world's most comprehensive resource for protein family, domain, and functional site classification.
LlamaIndex agents combine EBI InterPro tool responses with indexed documents for comprehensive, grounded answers. Connect 16 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Domain Classification — Retrieve detailed InterPro entry information including family/domain descriptions, GO terms, and member database cross-references
- Multi-Database Access — Query entries from Pfam, CDD, SMART, Prosite, PANTHER, Gene3D, HAMAP, and more through a unified interface
- Protein Annotation — Find all InterPro domains, families, and sites annotated on any UniProt protein
- Structure Mapping — Discover PDB structures containing proteins that match specific InterPro entries
- Taxonomic Distribution — Explore which organisms contain proteins matching a domain or family — essential for evolutionary biology
- Proteome Coverage — Assess domain annotation coverage for complete proteomes
- Clan Analysis — Navigate Pfam clan hierarchies to understand super-family relationships
The EBI InterPro MCP Server exposes 16 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 16 EBI InterPro tools available for LlamaIndex
When LlamaIndex connects to EBI InterPro through Vinkius, your AI agent gets direct access to every tool listed below — spanning interpro, pfam, protein-domains, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Get cdd entry on EBI InterPro
CDD provides curated models for protein domain families and includes additional alignment and structure data. Use accessions like cd00001. Get CDD (Conserved Domain Database) entry details
Get clan on EBI InterPro
Returns clan accession, name, description, and member counts. Use Pfam clan accessions like CL0001. Get Pfam clan (super-family grouping) details
Get entry on EBI InterPro
Returns name, type (family, domain, homologous superfamily, repeat, site), description, Gene Ontology terms, member database cross-references, and literature count. Use accessions like IPR000001, IPR036291. Get InterPro entry metadata for a family or domain
Get entry proteins on EBI InterPro
Returns protein accessions, names, lengths, and source organisms. Useful for finding all members of a protein family across the UniProt database. Get all proteins matching an InterPro entry
Get entry structures on EBI InterPro
Returns PDB IDs, names, experiment types, and resolutions. Useful for finding structural representatives of a protein family or domain. Get all PDB structures matching an InterPro entry
Get entry taxonomy on EBI InterPro
Returns taxonomy nodes with names, ranks, and protein counts. This answers the evolutionary biology question "which organisms have this domain/family?" and is essential for understanding protein evolution and conservation. Get taxonomic distribution of an InterPro entry
Get pfam entry on EBI InterPro
Pfam is the most widely used protein domain database. Use accessions like PF00069 (kinase domain), PF00076 (RRM domain). Get Pfam domain or family details
Get protein on EBI InterPro
Returns the protein name, length, source organism, evidence level, fragment status, and counters for how many InterPro entries, Pfam domains, structures, and taxa are associated with it. Get protein details with all domain and family assignments
Get protein entries on EBI InterPro
This is the key tool for understanding "what domains does my protein have?" — the fundamental question in protein characterization. Get all InterPro entries matching a specific protein
Get proteome on EBI InterPro
Returns proteome ID, organism name, strain, reference status, and counters for associated entries and proteins. Use UniProt proteome IDs like UP000005640 (human). Get proteome details with domain coverage statistics
Get structure on EBI InterPro
Use a 4-character PDB ID like 1cbs or 4hhb. Get a PDB structure with mapped InterPro annotations
Get taxonomy on EBI InterPro
Returns the organism name, rank, lineage, number of children taxa, and counters for associated InterPro entries and proteins. Use IDs like 9606 (human), 10090 (mouse), 562 (E. coli). Get taxonomic node with entry and protein counts
List entry databases on EBI InterPro
Shows the number of entries in each database. Useful for understanding the scope of available domain and family annotations. List all InterPro member databases and entry counts
Search entries on EBI InterPro
Optionally filter by entry type: family, domain, homologous_superfamily, repeat, or site. Returns accessions, names, types, and protein/structure counts. Use queries like "kinase", "zinc finger", "immunoglobulin". Search InterPro entries by keyword and type
Search proteins on EBI InterPro
Returns UniProt accessions, names, lengths, organisms, and annotation counts. Use queries like "insulin", "hemoglobin", "BRCA1". Search proteins in InterPro by name or keyword
Search taxonomy on EBI InterPro
Returns taxon IDs, names, ranks, and annotation counts. Use queries like "human", "drosophila", "arabidopsis", "saccharomyces". Search taxonomy by organism name
Connect EBI InterPro to LlamaIndex via MCP
Follow these steps to wire EBI InterPro into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the EBI InterPro MCP Server
LlamaIndex provides unique advantages when paired with EBI InterPro through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine EBI InterPro tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain EBI InterPro tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query EBI InterPro, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what EBI InterPro tools were called, what data was returned, and how it influenced the final answer
EBI InterPro + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the EBI InterPro MCP Server delivers measurable value.
Hybrid search: combine EBI InterPro real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query EBI InterPro to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying EBI InterPro for fresh data
Analytical workflows: chain EBI InterPro queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for EBI InterPro in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with EBI InterPro immediately.
"What domains and families does the human TP53 protein belong to?"
"Show me all member databases in InterPro and how many entries each has."
"Which organisms have the kinase domain PF00069?"
Troubleshooting EBI InterPro MCP Server with LlamaIndex
Common issues when connecting EBI InterPro to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEBI InterPro + LlamaIndex FAQ
Common questions about integrating EBI InterPro MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Explore More MCP Servers
View all →
Kisi
9 toolsManage cloud-based access control, locks, and users via the Kisi API.

Dutchie Plus
10 toolsEquip your AI agent to manage enterprise dispensary locations, track online menus, and monitor orders via the Dutchie Plus API.

Replicate Alternative
12 toolsRun ML models via Replicate — generate images, text, audio and video from community models, track predictions and explore collections from any AI agent.

Aliyun OSS / 阿里云对象存储
10 toolsChina's leading object storage service — manage files, buckets, and metadata via AI.
