How do I find my Orbit API Key?

In your Orbit account, go to **Settings** > **Account Settings**. Your API token is listed in the 'API Token' section.

Where is the Workspace ID located?

The Workspace ID is the unique slug found in the URL of your Orbit dashboard (e.g., `app.orbit.love/YOUR_WORKSPACE_ID`).

Can I add custom activities for members?

Yes! Use the `create_orbit_activity` tool. You can specify the `activity_type` and provide a link or unique key to record any community interaction.

Orbit MCP Server for LlamaIndex 0 tools — connect in under 2 minutes

Name: Orbit
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Orbit 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 Orbit. "
            "You have 0 tools available."
        ),
    )

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

asyncio.run(main())

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About Orbit MCP Server

Connect your Orbit workspace to any AI agent and take full control of your community management and engagement workflows through natural conversation.

LlamaIndex agents combine Orbit tool responses with indexed documents for comprehensive, grounded answers. Connect 0 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

Member Oversight — List all community members and retrieve detailed profiles, including social links and reach metrics.
Activity Tracking — Monitor the timeline of activities across your workspace and create new activities for members.
Organization Discovery — List and retrieve details for organizations associated with your community members.
Relationship Management — List and create notes on member profiles to maintain context on your interactions.
Engagement Insights — Fetch a detailed history of activities for any specific member to understand their journey.

The Orbit MCP Server exposes 0 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 Orbit to LlamaIndex via MCP

Follow these steps to integrate the Orbit 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 0 tools from Orbit

Why Use LlamaIndex with the Orbit MCP Server

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

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

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

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

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

Orbit + LlamaIndex Use Cases

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

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

Data enrichment: query Orbit 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 Orbit for fresh data

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

Example Prompts for Orbit in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Orbit immediately.

"List the last 5 members who joined our community."

"Show me the activity history for member 'john_doe'."

"Add a note to member 98765 saying 'Had a great call today about their upcoming blog post'."

Troubleshooting Orbit MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Orbit + LlamaIndex FAQ

Common questions about integrating Orbit 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.