Can the AI alter invoices or update CRM statuses remotely?

No, it cannot. The agent is explicitly configured to possess pure Read-Only access capability via API V2 GET methodology endpoints (Such as `list_companies` and `get_invoices`). Your operational data remains completely immutable regardless of what you prompt.

Can I query a specific estimate document generated for a client?

Yes. While the integration allows sweeping over lists using `list_estimates`, the LLM can interpret the raw data to give you the summary of any pending quotes directly tied to associated internal pipelines effortlessly.

Does it track sales activities like recent calls?

Yes—by prompting your LLM with the underlying `list_activities` directive, you immediately gain access to the chronological logs outlining calls, meetings, generic events, and tasks created by your native Sales Development Representatives (SDRs).

Sellsy MCP Server for LangChain 12 tools — connect in under 2 minutes

Name: Sellsy
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 12 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Sellsy through the 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({
        "sellsy": {
            "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 Sellsy, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 Sellsy MCP Server

Connect the Sellsy CRM API to your AI workflow to unlock conversational oversight over your entire French-designed commercial hub. By providing exactly Read-Only access, your agent can securely map ongoing deals, review invoice payment statuses, and fetch complete dossiers on existing catalog items and contacts.

LangChain's ecosystem of 500+ components combines seamlessly with Sellsy through native MCP adapters. Connect 12 tools via the 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

Client & Prospecting Analysis — Use natural language to search companies, retrieve full metadata via company_id, and pull associated granular contacts directly into the conversational context
Sales Pipeline Auditing — Ask the agent to list all active 'opportunities' and drill down into a specific Deal ID to review its exact stage and monetary potential
Billing Integrity — Prompt your LLM to sweep your current draft, sent, and overdue invoices, including exact estimates given out recently to big leads
CRM Activity Surveillance — Seamlessly extract chronological activity feeds (meetings, calls, tasks) to compile end-of-week reporting automatically

The Sellsy MCP Server exposes 12 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 Sellsy to LangChain via MCP

Follow these steps to integrate the Sellsy 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 12 tools from Sellsy via MCP

Why Use LangChain with the Sellsy MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine Sellsy 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 Sellsy queries for multi-turn workflows

Sellsy + LangChain Use Cases

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

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

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

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

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

Sellsy MCP Tools for LangChain (12)

These 12 tools become available when you connect Sellsy to LangChain via MCP:

get_company

Get detailed information about a specific company

get_contact

Get detailed information about a specific contact

get_deal

Get full details of a specific deal (amount, status, pipeline step, company)

get_invoice

Get full details of a specific invoice (amount, status, due date)

list_activities

List recent CRM activities (calls, emails, meetings, tasks)

list_companies

List all companies (clients, prospects) in the CRM

list_contacts

List all contacts in the CRM

list_deals

List all deals (opportunities) in the sales pipeline

list_estimates

List all estimates (quotes) sent to prospects

list_invoices

List all invoices (draft, sent, paid, overdue)

list_items

List all products and services in the catalog

search_companies

Search companies by name or keyword

Example Prompts for Sellsy in LangChain

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

"Identify pending Deals on Sellsy CRM and extract their projected monetary values."

"Pull the contact information and status for the primary user of 'Company XYZ'."

"Summarize the overarching status of my Sellsy invoices list."

Troubleshooting Sellsy MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Sellsy + LangChain FAQ

Common questions about integrating Sellsy 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.