How to Use the Wistia MCP in LlamaIndex

Q: Can I use the MCP Server tools with Wistia and LlamaIndex together?

Yes. You treat every tool call—whether it's listvideos or getprojectdetails—as a source document that gets added to your vector store.

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Connect Wistia MCP to LlamaIndex

Create your Vinkius account to connect Wistia 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 Wistia with LlamaIndex

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Indexing Wistia Project Data for LlamaIndex

Instead of just calling `list_projects`, you index the results. This means if you ask, 'What projects did we work on last quarter?', your RAG application searches past API output. LlamaIndex takes the structured data from tools like `get_account_analytics` and turns it into a searchable knowledge base, letting you query facts that aren't immediately available.

Advanced Wistia Video Context for LlamaIndex

The tool output for `list_videos` doesn't just give IDs; you can index them alongside their associated metadata from `get_video_details`. This combination lets your agent answer questions like, 'Which videos are older than six months and haven't been updated?' This is far better than simple keyword searching because the knowledge base understands the relationship between video status and date.

MCP Server for Wistia Reporting with LlamaIndex

Need to summarize performance? You can combine `get_video_stats` output with captions pulled via `list_video_captions`. The resulting text snippets are indexed into a unified document. Your FunctionAgent queries this index, giving you a summary grounded in the actual video analytics data, not just general knowledge.

Setup guide

Set up Wistia 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 Wistia 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 Wistia tools.",
)
response = await agent.run("List recent Wistia data")

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Common questions about Wistia MCP in LlamaIndex

It goes beyond simple lookups. By indexing tool output (like `get_account_analytics`), your agent can answer questions about past states of the MCP Server's data.

LlamaIndex indexes video metadata, project details, and performance analytics. This means you can search by criteria like project name or update date.

The system uses video metadata and account-wide stats. The MCP Server only exposes read access to this structured information for indexing.

Yes. You treat every tool call—whether it's `list_videos` or `get_project_details`—as a source document that gets added to your vector store.

The server touches video metadata, including titles and IDs. The focus is on structured reporting data, not private viewer content.

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OpenAI Agents SDK sdk-python