How to Use the ChartHop MCP in LlamaIndex

Q: How do I load ChartHop MCP tools into LlamaIndex?

Grab the llama-index-tools-mcp package via pip. Set up BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can I filter which ChartHop tools the LlamaIndex uses?

You control the access entirely. You pass an allowedtools array when setting up the spec, restricting the agent to safe operations like listorganizationteams while blocking sensitive planning data.

Index your live ChartHop employee directory and headcount plans straight into LlamaIndex for grounded HR search.

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Connect ChartHop MCP to LlamaIndex

Create your Vinkius account to connect ChartHop to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ChartHop with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index the Org Chart

RAG apps usually rely on stale PDFs. Connecting this MCP Server changes the equation by letting LlamaIndex ingest live employee rosters directly from `list_organization_people`. Your vector store suddenly knows exactly who works where. The agent pulls `list_organization_departments` and embeds the entire current org structure, making semantic searches about company hierarchy actually accurate.

Grounded Role Searches

Job descriptions change constantly. Instead of uploading static text files, your application uses `list_organization_jobs` to fetch the active roles. When a user asks about engineering requirements, the agent fires `get_job_details`. That fresh data gets chunked, embedded, and returned as a grounded answer, completely eliminating hallucinations about open reqs.

LlamaIndex MCP Server for Planning

Headcount planning requires context. Your knowledge base can now query `get_organization_summary` to understand the baseline before looking at future projections. The agent then pulls `list_planning_scenarios` to index proposed compensation models. Users can query their LlamaIndex app to compare Q4 hiring scenarios against the current baseline using real, embedded API responses.

Setup guide

Set up ChartHop MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all ChartHop MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to ChartHop tools.",
)
response = await agent.run("List recent ChartHop data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ChartHop. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ChartHop MCP in LlamaIndex

Grab the `llama-index-tools-mcp` package via pip. Set up `BasicMCPClient` with your Vinkius URL, wrap it in `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Not if you configure it correctly. By forcing the agent to query `get_person_details` before answering, the response is grounded in actual API output rather than training data guesses.

You control the access entirely. You pass an `allowed_tools` array when setting up the spec, restricting the agent to safe operations like `list_organization_teams` while blocking sensitive planning data.

The MCP protocol handles that translation natively. LlamaIndex reads the JSON schema exposed by the Vinkius endpoint and automatically converts it into Python tool signatures.

Vinkius endpoints require a single auth token and operate in a zero-trust environment. Whenever LlamaIndex fetches data from `list_planning_scenarios`, the connection is authenticated, processed in an isolated sandbox, and immediately destroyed.

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