How to Use the FreeAgent MCP in LlamaIndex

Q: How do I index FreeAgent data using LlamaIndex?

You use the LlamaIndex MCP tool spec to load tools like listbanktransactions into your agent. The agent executes these tools to fetch raw JSON, which LlamaIndex then converts into document nodes for your vector store.

Q: Can LlamaIndex query FreeAgent profit and loss statements?

Yes, the agent can call getprofitandloss to retrieve your real-time financial summary. LlamaIndex uses this structured output to answer complex analytical questions about your revenue and expenses.

Q: How do I prevent LlamaIndex from hallucinating financial numbers?

By forcing the agent to use tools like getinvoicedetails before answering. You configure the LlamaIndex router to retrieve fresh data from the API rather than relying on the LLM's static weights.

Q: How secure is my FreeAgent invoice metadata in LlamaIndex?

Your listinvoices data is processed entirely in memory within your local execution environment. We secure this pipeline by using encrypted transport layers and ephemeral sandboxes, ensuring your billing details are never exposed to public vector databases.

Index your FreeAgent financial records into LlamaIndex to query your ledger and P&L reports using natural language.

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Connect FreeAgent MCP to LlamaIndex

Create your Vinkius account to connect FreeAgent 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 FreeAgent with LlamaIndex

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Grounding RAG in Live FreeAgent Data

Don't let your agent guess about your balance sheet. This MCP Server exposes tools like `get_profit_and_loss` and `list_bank_accounts` directly to your indexing pipeline, turning raw financial data into searchable nodes. LlamaIndex ingest engines parse these live feeds to answer questions about your current cash position with absolute accuracy. Your queries are grounded in your actual ledger, not outdated spreadsheets or hallucinated figures.

Semantic Search Across Your Invoices

Finding specific transaction patterns is painful when you have hundreds of clients. By combining LlamaIndex vector stores with `list_invoices` and `get_invoice_details`, you can run semantic queries across all historical billing. Your agent searches through past descriptions, payment terms, and client notes to find matching records. This turns your flat invoice history into an active knowledge base that helps you predict future cash flow trends.

Contextual Expense Auditing with LlamaIndex

Auditing employee spending requires matching receipts to projects. You can point LlamaIndex at `list_out_of_pocket_expenses` and `list_projects` to build an automated compliance checker. The framework indexes the output of these tools, allowing your agent to cross-reference expenses against project budgets. You get instant alerts when out-of-pocket claims don't align with active client deliverables.

Setup guide

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

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Common questions about FreeAgent MCP in LlamaIndex

You use the LlamaIndex MCP tool spec to load tools like `list_bank_transactions` into your agent. The agent executes these tools to fetch raw JSON, which LlamaIndex then converts into document nodes for your vector store.

Yes, the agent can call `get_profit_and_loss` to retrieve your real-time financial summary. LlamaIndex uses this structured output to answer complex analytical questions about your revenue and expenses.

Yes, it integrates directly via the standard MCP protocol. You can expose all 12 endpoints, including `list_contacts` and `list_purchase_bills`, as standard tools for your LlamaIndex query engines.

By forcing the agent to use tools like `get_invoice_details` before answering. You configure the LlamaIndex router to retrieve fresh data from the API rather than relying on the LLM's static weights.

Your `list_invoices` data is processed entirely in memory within your local execution environment. We secure this pipeline by using encrypted transport layers and ephemeral sandboxes, ensuring your billing details are never exposed to public vector databases.

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