How to Use the EBI Proteins API MCP in LlamaIndex

Q: How do I load EBI Proteins API tools into LlamaIndex?

Install the tool package using pip install llama-index-tools-mcp and instantiate BasicMCPClient with your server URL. Wrap the client in McpToolSpec and call totoollistasync() to pass the tools to your FunctionAgent.

Q: Can I filter which EBI Proteins API tools are exposed to my LlamaIndex agent?

Yes, you use the allowedtools filter when defining your MCP tool specification. This lets you restrict your agent to specific operations like getprotein and searchproteins while ignoring proteomic data.

Q: Can this server populate a LlamaIndex Knowledge Graph?

Yes, your agent can call getgenecentric to map canonical proteins and then write these relationships directly into a Neo4j or Memgraph index using LlamaIndex property graph indexers.

Index live UniProt data directly into LlamaIndex vector stores to ground your RAG pipelines in real-time biology.

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Connect EBI Proteins API MCP to LlamaIndex

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

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Index Dynamic Protein Features for RAG Pipelines

This MCP Server connects your LlamaIndex pipelines directly to UniProt, translating live biochemical data into queryable index nodes. Your agent queries `get_protein_features` to extract active sites and domains, then indexes these annotations on the fly. Instead of relying on static, outdated documents, your RAG application retrieves fresh functional data from `get_mutagenesis`. This ensures your synthesis engine answers queries using verified experimental evidence.

Query Genetic Variants with Semantic Search

Turn raw variant data from `get_variation` into searchable vector embeddings within your index. Your agent retrieves clinical significance and consequence types, then structures the data for vector storage. When users ask natural language questions about specific somatic mutations, LlamaIndex matches their intent against these freshly indexed variations. The agent verifies the exact changes using `search_variation` before responding.

Ground Taxonomy and Proteome Queries via MCP Server

Avoid hallucinations in biological queries by grounding your model in taxonomic lineages fetched via `get_taxonomy`. The agent resolves taxon IDs dynamically before querying protein structures. You combine this taxonomic data with proteome metadata from `get_proteome` to build a structured knowledge graph. This setup ensures that your agent never mixes up mouse and human orthologs during synthesis.

Setup guide

Set up EBI Proteins API 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 Proteins API 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 Proteins API tools.",
)
response = await agent.run("List recent EBI Proteins API 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 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 LlamaIndex

Install the tool package using `pip install llama-index-tools-mcp` and instantiate `BasicMCPClient` with your server URL. Wrap the client in `McpToolSpec` and call `to_tool_list_async()` to pass the tools to your FunctionAgent.

Yes, you use the `allowed_tools` filter when defining your MCP tool specification. This lets you restrict your agent to specific operations like `get_protein` and `search_proteins` while ignoring proteomic data.

You should configure an intermediate Redis caching layer to handle peak volumes. If your RAG pipeline calls `get_proteomics` repeatedly during document ingestion, caching prevents triggering public API rate limits.

Yes, your agent can call `get_genecentric` to map canonical proteins and then write these relationships directly into a Neo4j or Memgraph index using LlamaIndex property graph indexers.

All requests containing NCBI taxon IDs, gene names, or protein sequences run through this MCP Server sandbox. Your query parameters are encrypted in transit and are never stored or used for training model checkpoints.

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