How to Use the NIH RePORTER (Research Funding) MCP in LlamaIndex

Q: How do I filter which MCP tools the agent can access?

Use the allowedtools parameter when setting up the agent. You can restrict it to just funding data or just publications depending on your use case.

Index federal grant data and publication records directly into your RAG applications with LlamaIndex.

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Connect NIH RePORTER (Research Funding) MCP to LlamaIndex

Create your Vinkius account to connect NIH RePORTER (Research Funding) 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 NIH RePORTER (Research Funding) with LlamaIndex

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Ground your RAG in actual grant dollars

The `search_projects` tool extracts hard numbers on NIH funding, principal investigators, and host organizations. Your LlamaIndex application pulls this raw data and indexes it straight into your vector store. You stop relying on outdated training data for financial questions. When a user asks about recent neurology grants, the system queries the live API and grounds its answer in the exact federal records.

Index publication metadata with this MCP Server

The `search_publications` tool grabs the scientific papers tied to specific federal grants. You pass a project ID, and the tool returns the metadata for every published result. LlamaIndex turns these API responses into searchable documents. You build a knowledge base where funding inputs and scientific outputs live side-by-side, ready for semantic search.

Query past funding sessions

You don't just call the tool once and lose the data. LlamaIndex stores the retrieved grant and publication records so you can query them later. This builds a persistent, domain-specific index. Your users get answers backed by actual NIH RePORTER data, and you eliminate the risk of the model inventing fake grant numbers.

Setup guide

Set up NIH RePORTER (Research Funding) 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 NIH RePORTER (Research Funding) 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 NIH RePORTER (Research Funding) tools.",
)
response = await agent.run("List recent NIH RePORTER (Research Funding) 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 NIH RePORTER. 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 NIH RePORTER (Research Funding) MCP in LlamaIndex

Install llama-index-tools-mcp. Set up a BasicMCPClient pointing to the server URL, wrap it in an McpToolSpec, and pass the tools to your FunctionAgent.

It indexes the responses. If you run a search for cardiology grants, the framework converts the JSON output into nodes and stores them in your vector database for future semantic queries.

Yes. You can build an index that merges live NIH grant data with your internal research documents. The agent queries both sources to answer complex questions.

Use the allowed_tools parameter when setting up the agent. You can restrict it to just funding data or just publications depending on your use case.

The server handles public investigator names and institution details. Vinkius routes these requests through a zero-trust, ephemeral sandbox, so your specific RAG indexing queries are never stored or logged on our infrastructure.

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