How to Use the Holded MCP in LlamaIndex

Q: How do I index Holded data using LlamaIndex?

Use the McpToolSpec to fetch data from the server. Once the tools are available, you pass the output of tools like listinvoices directly into your ingestion pipeline.

Q: Can LlamaIndex query my Holded expenses?

It can run listexpenses and inject the results into your vector index. Your agent then performs semantic search over these records to answer your queries.

Index your Holded business data for RAG with LlamaIndex. Turn your invoices and products into a searchable knowledge base.

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Connect Holded MCP to LlamaIndex

Create your Vinkius account to connect Holded 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 Holded with LlamaIndex

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Semantic search across Holded records

Feed the output of `list_products` and `list_quotes` into your vector store. Your LlamaIndex agent retrieves relevant inventory data before it answers a question. This creates a grounded knowledge base. You stop guessing what is in stock and start querying your live database.

Ground AI responses in Holded financial data

Connect your index to `list_purchase_invoices` to provide context for your agent. It references actual expense records when summarizing your monthly burn rate. This approach eliminates hallucinations. Every answer the agent provides is backed by the data it fetched from your account.

Unified querying with LlamaIndex MCP tools

Use `McpToolSpec` to bridge your API data and your documents. Your agent calls `list_contacts` to identify the right client before searching through related invoices. Everything happens in one workflow. You build a system that understands both your unstructured files and your structured ERP data.

Setup guide

Set up Holded 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 Holded 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 Holded tools.",
)
response = await agent.run("List recent Holded 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 Holded. 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 Holded MCP in LlamaIndex

Use the `McpToolSpec` to fetch data from the server. Once the tools are available, you pass the output of tools like `list_invoices` directly into your ingestion pipeline.

It can run `list_expenses` and inject the results into your vector index. Your agent then performs semantic search over these records to answer your queries.

No, your financial data remains in your private index. The agent only uses the data to provide context for your current session.

The agent receives a standard error response if a call like `list_products` times out. You define the retry logic within your `FunctionAgent` configuration.

Your CRM data is encrypted in transit and never stored outside your chosen vector store. Access is restricted to the tools you explicitly authorize.

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