Frontify MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Frontify as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Frontify. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Frontify?"
)
print(response)
asyncio.run(main())
* 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 Frontify MCP Server
Connect your Frontify account to any AI agent and take full control of your digital asset management (DAM), brand guidelines, and collaborative workspaces through natural conversation.
LlamaIndex agents combine Frontify tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Workspace Project Orchestration — Enumerate explicitly registered project schemas and gather required IDs to browse and discover collaborative workspaces natively
- Asset Lifecycle Management — Retrieve detailed metadata for project assets and perform structural extraction of properties driving active media limits flawslessly
- Brand Guideline Discovery — Identify precise active arrays spanning rented documentation trees, identifying where strict UI/UX constraints and brand rules are registered
- Metadata Mutation — Update global asset boundaries by substituting attributes like titles and descriptions securely through GraphQL mutation logic
- Media Content Oversight — Analyze specific global boundaries iterating through brands to discover exact tenant separations inside a single account
- Identity & User Management — Retrieve the exact structural matching verifying identity schemas and invite new users directly into designated project workspaces securely
- Digital Asset Purging — Irreversibly vaporize explicit app nodes to remove media assets and separating limits pulling items offline flawlessly
- Custom GraphQL Execution — Identify bounded routing spaces inside the headless Frontify DAM utilizing native GraphQL strings for advanced structural queries
The Frontify MCP Server exposes 10 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 Frontify to LlamaIndex via MCP
Follow these steps to integrate the Frontify 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 10 tools from Frontify
Why Use LlamaIndex with the Frontify MCP Server
LlamaIndex provides unique advantages when paired with Frontify through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Frontify tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Frontify tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Frontify, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Frontify tools were called, what data was returned, and how it influenced the final answer
Frontify + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Frontify MCP Server delivers measurable value.
Hybrid search: combine Frontify real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Frontify 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 Frontify for fresh data
Analytical workflows: chain Frontify queries with LlamaIndex's data connectors to build multi-source analytical reports
Frontify MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Frontify to LlamaIndex via MCP:
execute_graphql_payload
Identify bounded routing spaces inside the Headless Frontify DAM utilizing native GraphQL strings
get_account_limits
Inspect deep internal arrays mitigating specific Picture constraints
get_project_assets
Retrieve explicit Cloud logging tracing explicit Asset Limits
invite_workspace_user
Dispatch an automated validation check routing explicit Workspace roles
list_brand_guidelines
Identify precise active arrays spanning rented Documentation trees
list_native_brands
Perform structural extraction of properties driving active Global namespaces
list_platform_users
Retrieve the exact structural matching verifying Identity schemas
list_workspace_projects
Enumerate explicitly attached structured rules exporting active Workspaces
patch_asset_metadata
Mutate global Web CRM boundaries substituting Attributes safely
wipe_media_asset
Irreversibly vaporize explicit App nodes dropping live Database bytes
Example Prompts for Frontify in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Frontify immediately.
"List all projects in my Frontify workspace"
"Show me the brand guidelines for 'Acme Corp'"
"Invite 'designer@example.com' to project 'abc-123'"
Troubleshooting Frontify MCP Server with LlamaIndex
Common issues when connecting Frontify to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFrontify + LlamaIndex FAQ
Common questions about integrating Frontify MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Frontify with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Frontify to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.