Can I see all open job positions in my company?

Yes! Use the `list_organization_jobs` tool. The agent will return all roles, and you can filter or identify those that are currently marked as open or unfilled.

How do I check the reporting line for a specific person?

Use the `get_person_details` tool with the person's unique ID. Your agent will fetch the full profile, which includes their manager and organizational placement data.

Where do I find my ChartHop Org ID and API Token?

You can find your Org ID in the URL when logged into ChartHop. To get an API Token, you typically need to create a 'ChartHop App' or request one from your administrator.

ChartHop MCP Server for LangChain 8 tools — connect in under 2 minutes

Name: ChartHop
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect ChartHop 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({
        "charthop": {
            "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 ChartHop, 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 ChartHop MCP Server

Connect your ChartHop account to any AI agent and take full control of your organizational data and workforce planning through natural conversation. Streamline how you manage your roster and headcount.

LangChain's ecosystem of 500+ components combines seamlessly with ChartHop through native MCP adapters. Connect 8 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

Roster Oversight — List and retrieve details for all people and filled roles in your organization natively
Headcount Planning — Access and monitor open job positions and headcount scenarios flawlessly
Organizational Mapping — List departments, teams, and their hierarchical structures securely
Deep-Dive Profiles — Retrieve complete person information, including job history and compensation metadata flawlessly
Scenario Visibility — Access and review headcount and compensation planning scenarios in real-time
System Intelligence — Retrieve core organization information and account settings directly within your workspace

The ChartHop MCP Server exposes 8 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 ChartHop to LangChain via MCP

Follow these steps to integrate the ChartHop 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 8 tools from ChartHop via MCP

Why Use LangChain with the ChartHop MCP Server

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

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

ChartHop + LangChain Use Cases

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

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

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

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

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

ChartHop MCP Tools for LangChain (8)

These 8 tools become available when you connect ChartHop to LangChain via MCP:

get_job_details

Get detailed information for a specific job

get_organization_summary

Retrieve core organization information and settings

get_person_details

Get detailed profile information for a specific person

list_organization_departments

List all departments in the organization

list_organization_jobs

List all jobs (roles) in the organization

list_organization_people

List all people (employees) in the organization

list_organization_teams

List all teams in the organization

list_planning_scenarios

List headcount and compensation planning scenarios

Example Prompts for ChartHop in LangChain

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

"Show me the headcount summary for my organization."

"List all departments and their leaders."

"Show me details for 'John Smith' in ChartHop."

Troubleshooting ChartHop MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

ChartHop + LangChain FAQ

Common questions about integrating ChartHop 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.