How to Use the freee MCP in LlamaIndex

Q: How do I avoid hallucinations when querying freee via LlamaIndex?

Feed getpartner and listaccounts outputs directly into your LlamaIndex response synthesizer. Grounding the context in real-time API payloads ensures the agent never invents fake ledger entries.

Q: How does LlamaIndex handle large lists of invoices?

Use the allowedtools filter to limit agent access to specific tools like listinvoices. This optimizes token usage and speeds up indexing by ignoring irrelevant endpoints during focused runs.

Q: Where is my Japanese invoice and tax data stored?

Your financial transactions retrieved via listinvoices and listtaxes are processed in an ephemeral sandbox. No data is cached or stored by the hosting platform, keeping your corporate ledger private.

Build RAG pipelines that index your Japanese ledger directly into LlamaIndex using the freee MCP Server.

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Connect freee MCP to LlamaIndex

Create your Vinkius account to connect freee 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 freee with LlamaIndex

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Semantic search over Japanese ledger data

`list_deals` retrieves your transaction history so LlamaIndex can index the raw text directly into a vector store. Users query the index using natural language to find specific financial patterns without writing SQL. This integration turns your live accounting records into a searchable knowledge base. Your agent answers complex financial questions by combining historical documents with live data from `get_deal`.

Grounded tax code querying in LlamaIndex

`list_taxes` pulls official Japanese tax classifications directly into your LlamaIndex RAG pipeline. Your system matches these codes against transaction descriptions to prevent hallucinations. By using the MCP tool specification adapter, you expose live tax rules to your index. This guarantees that your agent's compliance answers are grounded in actual tax data rather than pre-trained weights.

Automated partner profiles

`list_partners` fetches supplier directories to enrich your LlamaIndex knowledge graphs. The framework links each supplier with transaction histories retrieved from `list_invoices` for complete context. You implement this using the basic MCP client to build a unified index of your business relationships. Your queries retrieve structured partner data and financial history in a single execution.

Setup guide

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

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Common questions about freee MCP in LlamaIndex

Yes, you can use the freee MCP Server to extract live transactions via `list_deals` and build a vector index. This allows semantic search over your entire Japanese financial history.

Feed `get_partner` and `list_accounts` outputs directly into your LlamaIndex response synthesizer. Grounding the context in real-time API payloads ensures the agent never invents fake ledger entries.

Yes, use llama-index-tools-mcp to load the freee MCP Server tools directly into your agent. This exposes the full suite of accounting tools, including `list_walletables`, in a few lines of code.

Use the `allowed_tools` filter to limit agent access to specific tools like `list_invoices`. This optimizes token usage and speeds up indexing by ignoring irrelevant endpoints during focused runs.

Your financial transactions retrieved via `list_invoices` and `list_taxes` are processed in an ephemeral sandbox. No data is cached or stored by the hosting platform, keeping your corporate ledger private.

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