Can my AI actively search for a specific customer before creating an invoice?

Yes. It can sequentially execute `search_customers` to find the precise Unique ID of the customer using fuzzy name matching, and subsequently inject that accurate payload into `create_document` without human error.

Is 'list_stocks' showing data in real-time?

Yes. Since this operates as a direct pass-through via the MCP to the PHC GO platform API, all stock matrices, warehouse item counts, and current accounts represent the exact live state of your corporate database in the split second the AI requests it.

Can I query products that exist across multiple warehouses?

Absolutely. By commanding the agent to align returns from both `list_products` and `list_warehouses` against `list_stocks`, your LLM can map out a matrix comparing which warehouse holds the higher volume of the targeted SKUs.

PHC GO MCP Server for LangChain 16 tools — connect in under 2 minutes

Name: PHC GO
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 16 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect PHC GO through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "phc-go": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using PHC GO, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About PHC GO MCP Server

Connect your PHC GO enterprise ecosystem to your AI agent and manage operations, invoicing, and logistics through natural language commands natively.

LangChain's ecosystem of 500+ components combines seamlessly with PHC GO through native MCP adapters. Connect 16 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Entity Management — Retrieve master data such as list_customers and natively create_customer directly from your chat context.
Inventory & Logistics — Pull active article sheets with list_products, or audit material counts instantly using list_stocks and list_warehouses.
Billing & Finance — Dynamically execute the create_document endpoint to issue invoices/estimates, and reconcile using list_current_accounts and list_taxes.
Sales Ecosystem — Quickly fetch configuration records like list_series and list_payment_methods to standardize business logic via intelligent agents.

The PHC GO MCP Server exposes 16 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect PHC GO to LangChain via MCP

Follow these steps to integrate the PHC GO MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 16 tools from PHC GO via MCP

Why Use LangChain with the PHC GO MCP Server

LangChain provides unique advantages when paired with PHC GO through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine PHC GO MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across PHC GO queries for multi-turn workflows

PHC GO + LangChain Use Cases

Practical scenarios where LangChain combined with the PHC GO MCP Server delivers measurable value.

RAG with live data: combine PHC GO tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query PHC GO, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain PHC GO tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every PHC GO tool call, measure latency, and optimize your agent's performance

PHC GO MCP Tools for LangChain (16)

These 16 tools become available when you connect PHC GO to LangChain via MCP:

create_customer

Criar um novo cliente/entidade no PHC GO

create_document

Criar um novo documento comercial no PHC GO (fatura, orçamento, encomenda, etc)

create_product

Criar um novo artigo ou serviço no PHC GO

get_customer_details

Obter detalhes completos de um cliente/entidade pelo ID

get_document_details

Obter detalhes completos de um documento comercial (fatura, nota crédito, recibo, etc) incluindo linhas

get_product_details

Obter detalhes de um artigo/serviço pelo ID

list_current_accounts

Consultar a conta corrente de clientes (saldos e movimentos pendentes)

list_customers

Listar todas as entidades/clientes registados no PHC GO

list_documents

Listar documentos comerciais do PHC GO por tipo (FT=Fatura, NC=Nota Crédito, RC=Recibo, ORC=Orçamento, ECL=Encomenda, GR=Guia Remessa)

list_payment_methods

Listar métodos de pagamento configurados (Transferência, MB, MBWay, Cheque)

list_products

Listar todos os artigos e serviços registados no PHC GO

list_series

Listar séries de numeração de documentos (FT 2026/, NC 2026/, etc)

list_stocks

Consultar movimentos de stock. Opcionalmente filtrar por artigo

list_taxes

Listar taxas de IVA e impostos configurados no PHC GO

list_warehouses

Listar armazéns configurados para gestão de stock

search_customers

Pesquisar clientes por nome, NIF ou código

Example Prompts for PHC GO in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with PHC GO immediately.

"Check the available stock for 'Macbook Pro M3' across all warehouses."

"Create a new customer profile for 'Acme Corp' and fetch their assigned customer ID."

"Examine our outstanding current accounts from January to spot pending vendor debts."

Troubleshooting PHC GO MCP Server with LangChain

Common issues when connecting PHC GO to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

PHC GO + LangChain FAQ

Common questions about integrating PHC GO MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.