Can the agent calculate how many hours were logged on a specific project?

Yes. If you ask your agent 'How many hours were logged for project Alpha recently?', the agent will invoke `list_time_entries` and filter the returned data by that specific project, summing up the billable minutes natively.

Is it possible to track unpaid invoices directly from the chat?

Absolutely. Just prompt the AI: 'List my recent sales invoices and flag any that haven't been paid'. The agent will make an API call to `list_invoices` and analyze the 'payment status' attributes for you.

Can I retrieve contact information before a meeting?

Yes. You can use the agent to prepare yourself. Prompt: 'Search our contacts for Acme Corp and get me their full profile'. The agent relies on `list_contacts` and `get_contact` to pull in emails, phone numbers, and relational tags.

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

Name: Scoro
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 Scoro 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({
        "scoro": {
            "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 Scoro, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Scoro MCP Server

Connect your Scoro workspace to any AI agent to interact with your business operations securely. Avoid context-switching between your agency's CRM, PM software, and billing dashboards.

LangChain's ecosystem of 500+ components combines seamlessly with Scoro through native MCP adapters. Connect 12 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

Project & Task Tracking — Query your entire project portfolio, check on ongoing task statuses, and extract time entries logged by the team
CRM & Contacts — Retrieve details, metadata, and history for any client or vendor in your database
Financial Health — List sales invoices, pending quotes, and registered business expenses to track profitability without running manual reports
Team Management — Consult your user roster, pull recent activity audit logs, and browse team calendar events directly within your chat environment

The Scoro 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 Scoro to LangChain via MCP

Follow these steps to integrate the Scoro 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 Scoro via MCP

Why Use LangChain with the Scoro MCP Server

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

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

Scoro + LangChain Use Cases

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

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

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

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

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

Scoro MCP Tools for LangChain (12)

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

get_contact

Retrieves details for a specific contact

list_activities

Lists recent activities and audit logs

list_calendar_events

Lists all events on the Scoro calendar

list_contacts

Lists all contacts (companies and people) in Scoro

list_expenses

Lists all business expenses

list_invoices

Lists all sales invoices

list_products

Lists all products and services in the catalog

list_projects

Lists all projects in Scoro

list_quotes

Lists all sales quotes

list_tasks

Lists all tasks

list_time_entries

Lists time entries logged by the team

list_users

Lists all users in the Scoro organization

Example Prompts for Scoro in LangChain

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

"What are the most recent activities logged in my Scoro workspace?"

"List all active marketing projects and their statuses."

"Show me our total revenue from the latest invoices."

Troubleshooting Scoro MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Scoro + LangChain FAQ

Common questions about integrating Scoro 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.