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 LlamaIndex 12 tools — connect in under 2 minutes

Name: Sellsy
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Sellsy as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Sellsy. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Sellsy?"
    )
    print(response)

asyncio.run(main())

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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.

LlamaIndex agents combine Sellsy tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

Follow these steps to integrate the Sellsy MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Sellsy

Why Use LlamaIndex with the Sellsy MCP Server

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

Data-first architecture: LlamaIndex agents combine Sellsy tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Sellsy tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Sellsy, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Sellsy tools were called, what data was returned, and how it influenced the final answer

Sellsy + LlamaIndex Use Cases

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

Hybrid search: combine Sellsy real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Sellsy to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Sellsy for fresh data

Analytical workflows: chain Sellsy queries with LlamaIndex's data connectors to build multi-source analytical reports

Sellsy MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Sellsy to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Sellsy + LlamaIndex FAQ

Common questions about integrating Sellsy MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Sellsy tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.