How to Use the oboloo MCP in LlamaIndex

Q: How do I filter oboloo tools in LlamaIndex?

Use the allowedtools filter during your setup. This limits the agent to only the specific procurement functions you want it to access.

Index your oboloo procurement data into LlamaIndex knowledge bases.

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Connect oboloo MCP to LlamaIndex

Create your Vinkius account to connect oboloo 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 oboloo with LlamaIndex

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Perplexity

Index procurement records in LlamaIndex

Convert `list_suppliers` and `list_projects` output into searchable vector indices. Your agent stops hallucinating and starts answering based on actual records. This turns your static procurement data into a queryable knowledge base. You get precise answers backed by your own supplier history.

Ground answers in API data

Use `get_supplier` to pull specific details into your RAG pipeline. The MCP Server acts as the bridge between your live API and the search index. Your agent retrieves context from these tools before generating a response. It stays grounded in the facts currently residing in your account.

Query past procurement sessions

Run `list_savings` to populate your index with historical performance data. You can search these results to find patterns in your spending. LlamaIndex stores this information alongside your documents for unified retrieval. It makes finding specific cost-saving events nearly instantaneous.

Setup guide

Set up oboloo 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 oboloo 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 oboloo tools.",
)
response = await agent.run("List recent oboloo data")

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