How to Use the Workvivo MCP in LlamaIndex

Q: How do I use Workvivo with LlamaIndex to manage posts?

First, you call listworkvivoposts to gather the data. Then, passing that output into your RAG pipeline indexes the content. You can then query historical details about any post using semantic search against the indexed knowledge base.

Q: Can Workvivo and LlamaIndex help me track events?

Yes. Use listcompanyevents to pull all event data into your index. Later, you can query specific details by asking natural language questions that are answered based on the indexed event information.

Q: If I use Workvivo and LlamaIndex, how do I handle deletions?

You would first retrieve the post ID using getpostdetails, and then run the deletion process. The index itself is updated to reflect that the data has been removed from the source.

Ground your knowledge base with LlamaIndex: Indexing Workvivo data for semantic search results.

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Connect Workvivo MCP to LlamaIndex

Create your Vinkius account to connect Workvivo 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 Workvivo with LlamaIndex

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Search historical Workvivo posts using the MCP Server

Use `list_workvivo_posts` to gather a batch of social activity from Workvivo. LlamaIndex then takes that raw data and indexes it into your vector store, making every post searchable by meaning, not just keywords. After indexing, you can query past sessions or posts using semantic search against the indexed records. This lets you find answers grounded in actual API data—not hallucinations.

Build a knowledge base of employee details

You start by calling `list_employees` to pull all available user profiles into the index. Once these are stored, your RAG application can answer complex questions like 'Who works in Finance and attended the last event?' using only the indexed data. This mechanism means you don't just get a list; you get contextual answers derived from the combined profile information.

Retrieve details on Workvivo spaces for RAG

Need to know what groups exist? Run `list_collaboration_spaces` and then index the results. This makes the group structure part of your searchable knowledge base, allowing you to ask questions like 'What are all the major collaboration areas?' The LlamaIndex setup ensures that the full context of every space is available for later retrieval.

Setup guide

Set up Workvivo 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 Workvivo 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 Workvivo tools.",
)
response = await agent.run("List recent Workvivo 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 Workvivo. 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 Workvivo MCP in LlamaIndex

First, you call `list_workvivo_posts` to gather the data. Then, passing that output into your RAG pipeline indexes the content. You can then query historical details about any post using semantic search against the indexed knowledge base.

Yes. Use `list_company_events` to pull all event data into your index. Later, you can query specific details by asking natural language questions that are answered based on the indexed event information.

The server touches employee profile details, social posts, and space details. When configuring your index, you control what parts of these specific data types are ingested into your searchable knowledge base.

It does. You can combine the outputs from multiple tool calls—like listing employees and then getting event details—and index them together to form a single, comprehensive knowledge record.

You would first retrieve the post ID using `get_post_details`, and then run the deletion process. The index itself is updated to reflect that the data has been removed from the source.

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