How to Use the Certify (Emburse) MCP in LlamaIndex

Q: How do I load Certify (Emburse) tools into LlamaIndex?

Install llama-index-tools-mcp and instantiate the BasicMCPClient with your server URL. Wrap it in a McpToolSpec and call totoollistasync() to get tools for your agent.

Q: Can I filter which Certify (Emburse) tools LlamaIndex can access?

Yes, you can use the allowedtools filter when initializing the client. This lets you restrict your LlamaIndex agent to only read receipts or check invoices while ignoring employee list tools.

Q: Does this integration support resource loading?

Yes, by setting includeresources=True in your client config, LlamaIndex can directly read and index raw data resources exposed by the server.

Index your Certify (Emburse) receipts and expenses directly into LlamaIndex for grounded RAG queries.

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Connect Certify (Emburse) MCP to LlamaIndex

Create your Vinkius account to connect Certify (Emburse) 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 Certify (Emburse) with LlamaIndex

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Query your Certify MCP Server with LlamaIndex RAG

This MCP server uses `list_expense_reports` and `list_certify_expenses` to pull live financial records into your LlamaIndex vector store. Your agent indexes these outputs to answer natural language queries about company spend patterns. Instead of guessing, the agent searches the vector index of your actual expense reports. This grounds every response in real data, preventing hallucinations about what your team spent.

Index invoices and receipts for semantic search

This MCP server exposes `list_certify_invoices` and `list_certify_receipts` to build search indexes of your accounts payable documents. LlamaIndex ingests these records, allowing your agent to locate specific transactions based on contextual descriptions. Your agent searches through receipt metadata and invoice details to find matching records. This mechanism replaces traditional keyword searches with semantic understanding of your financial files.

Map GL dimensions to department structures

This MCP server provides `get_gl_dimensions` and `list_certify_departments` to structure your financial context within LlamaIndex. Your agent reads these dimensions to organize indexed expense data by department. This organization allows the agent to construct queries that filter spend by specific business units. It ensures that your automated financial reports align perfectly with your existing general ledger setup.

Setup guide

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

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Common questions about Certify (Emburse) MCP in LlamaIndex

Install `llama-index-tools-mcp` and instantiate the `BasicMCPClient` with your server URL. Wrap it in a `McpToolSpec` and call `to_tool_list_async()` to get tools for your agent.

Yes, you can use the `allowed_tools` filter when initializing the client. This lets you restrict your LlamaIndex agent to only read receipts or check invoices while ignoring employee list tools.

Your agent calls the tools dynamically to fetch fresh data like `list_expense_reports`. You can then re-index the latest outputs into your vector store to keep your search results current.

Yes, by setting `include_resources=True` in your client config, LlamaIndex can directly read and index raw data resources exposed by the server.

Your GL dimensions, employee lists, and invoices are processed inside a secure V8 isolate sandbox. LlamaIndex only indexes the text outputs returned by the tools, and you control where that vector index is stored.

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