How to Use the FreeAgent MCP in LangChain

Q: How do I pass FreeAgent tool outputs to subsequent LangChain steps?

The output from tools like getprofitandloss is returned as structured text directly to the agent. LangChain's conversational memory or state graph passes this JSON data to the next node in your chain for further processing.

Feed FreeAgent ledger data directly into LangChain chains to automate multi-step reconciliation and P&L analysis.

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Connect FreeAgent MCP to LangChain

Create your Vinkius account to connect FreeAgent to LangChain 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 LangChain

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Claude

Perplexity

Chain-Linked Reconciliation in LangChain

Stop copying transaction hashes manually. This MCP Server exposes your banking feeds directly to your chains, letting you run `list_bank_accounts` and pull matching records with `list_bank_transactions` in a single run. Your agent inspects the outputs, catches unmatched items, and decides whether to query `list_invoices` to close the loop. You track every single tool call inside LangSmith to debug exactly why an invoice didn't match a deposit.

Traceable Financial Pipelines

LangChain agents excel at multi-step reasoning, which is exactly what you need when parsing complex ledgers. You can build a pipeline that starts with `get_profit_and_loss` to identify margin anomalies before digging deeper. The agent then automatically triggers `list_purchase_bills` or `list_out_of_pocket_expenses` to pinpoint where the cash went. Every step of this financial investigation is logged, giving you a clear audit trail of how the agent reached its conclusions.

Context-Aware Client Management

Connect your customer workflows directly to your accounting database. By combining LangChain vector search with `list_contacts` and `get_contact_details`, your agent knows exactly who owes you money before drafting a follow-up. The agent pulls outstanding balances using `get_invoice_details` and drafts personalized emails based on actual payment histories. You get a system that acts on real-time financial context without manual intervention.

Setup guide

Set up FreeAgent MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes FreeAgent tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "freeagent-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent FreeAgent transactions"
    })
    print(result["messages"][-1].content)

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Common questions about FreeAgent MCP in LangChain

Yes, you can. You connect the server using the LangChain MCP adapter, which registers all 12 accounting tools directly into your agent's toolkit. This lets your chain call tools like `list_invoices` dynamically based on the user's prompt.

You should handle rate limits within your LangChain runnable configuration or agent executor. When tools like `list_bank_transactions` return rate limit errors, you can configure standard retry logic with exponential backoff directly in your chain definition.

This specific server focuses on read operations to keep your ledger safe. Your LangChain agents can fetch details using tools like `get_invoice_details` or `list_purchase_bills`, but they cannot modify or delete your financial records.

The output from tools like `get_profit_and_loss` is returned as structured text directly to the agent. LangChain's conversational memory or state graph passes this JSON data to the next node in your chain for further processing.

Your bank transactions and company metadata stay strictly within your local environment and the secure Vinkius sandbox. This integration never stores your raw `list_bank_transactions` payload or credentials on external servers, routing them only to your specified LLM provider.

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