How do I get an Access Token for Frame.io?

You can generate a Personal Access Token in the Frame.io Developer Portal at https://developer.frame.io.

Can I add comments with a specific timecode using the agent?

Yes! The 'add_comment' tool accepts an optional 'timestamp' parameter in seconds, allowing you to pin your feedback to a specific moment in the video.

How do I see the assets inside a specific folder?

Use the 'list_assets' tool and provide the Folder ID as the 'parentId'. The agent will return all files and sub-folders contained within that directory.

Is it possible to list external review links?

Yes! Use the 'list_review_links' tool and provide the Project ID. The agent will retrieve all active shared links for that project.

Frame.io MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Frame.io
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Frame.io 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 Frame.io. "
            "You have 12 tools available."
        ),
    )

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

asyncio.run(main())

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* 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 Frame.io MCP Server

Connect your Frame.io account to any AI agent to automate your video collaboration and creative workflows through the Model Context Protocol (MCP). Frame.io is the industry-leading platform for reviewing and approving media, allowing teams to stay in sync from anywhere in the world. This MCP server enables you to manage your projects, retrieve asset metadata, and participate in time-coded discussions directly through natural conversation.

LlamaIndex agents combine Frame.io tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.

Key Features

Project Oversight — List all projects within your teams and fetch detailed metadata including ownership and status.
Asset Management — List files and folders within projects and retrieve complete metadata for specific media assets.
Collaborative Feedback — List all comments on an asset and add new time-coded feedback directly from your chat interface.
Review Coordination — Access and list review links to monitor how your media is being shared with external stakeholders.
Team Interaction — List team members and collaborators to maintain full context of who is involved in each project.
Directory Structure — Navigate through folders and sub-folders within your project library to organize your work effectively.
Real-time Monitoring — Fetch specific asset details or comments to keep your post-production workflow moving fast.

The Frame.io MCP Server exposes 12 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 Frame.io to LlamaIndex via MCP

Follow these steps to integrate the Frame.io 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 12 tools from Frame.io

Why Use LlamaIndex with the Frame.io MCP Server

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

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

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

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

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

Frame.io + LlamaIndex Use Cases

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

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

Data enrichment: query Frame.io 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 Frame.io for fresh data

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

Frame.io MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Frame.io to LlamaIndex via MCP:

add_comment

Post a new comment

get_asset_details

Get asset metadata

get_my_profile

Get current user profile

get_project_details

Get project metadata

list_accounts

List billing accounts

list_asset_comments

List comments on an asset

list_assets

List assets or folder contents

list_collaborators

List project collaborators

list_folders

List folders in project

list_projects

List projects in a team

list_review_links

List project review links

list_teams

List Frame.io teams

Example Prompts for Frame.io in LlamaIndex

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

"List all my projects in Frame.io team 'team_abc123'."

"Show me the last 5 comments on video asset 'vid_9876'."

"Add a comment to 'vid_9876': 'Great work, let\'s proceed to export' at 120 seconds."

Troubleshooting Frame.io MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Frame.io + LlamaIndex FAQ

Common questions about integrating Frame.io 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.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Frame.io tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.