How do I get my Framer API key?

Open your Framer project in the editor. Go to **Site Settings** (gear icon in the top bar), then navigate to the **General** tab. Scroll down to find the **Server API** section and click **Generate API key**. Copy the key and store it securely — treat it like a password. This key is specific to this project. Paste it into the configuration field below.

Can I push content from a spreadsheet into my Framer CMS?

Yes. Your AI agent can create and update CMS collection items programmatically. Describe the data you want to add, and it maps your fields to the Framer collection schema and creates the items — perfect for bulk content migrations or data-driven landing pages.

Does publishing through the API affect my Framer design?

No. The Server API only manages CMS content — it cannot modify your visual design, canvas layout, or component structure. Your designers remain in full control of the visual experience while content teams operate independently through the AI agent.

Is this suitable for agencies managing multiple Framer projects?

Yes. Each API key is bound to a specific Framer project. Configure separate integrations for each client project and your AI agent can switch between them — managing content across your entire portfolio without opening multiple browser tabs.

Framer MCP Server for LlamaIndex 8 tools — connect in under 2 minutes

Name: Framer
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Framer 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 Framer. "
            "You have 8 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Framer?"
    )
    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 Framer MCP Server

Connect Framer to your AI agent and manage your website CMS content and publishing workflow conversationally.

LlamaIndex agents combine Framer tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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

CMS Collections — List, create, update, and manage CMS collection items directly from natural language commands.
Content Sync — Push content updates from external data sources into your Framer CMS collections programmatically.
Site Publishing — Trigger site publishes to push your latest CMS changes live.
Collection Schema — Query collection structures and field definitions to understand your content model.

The Framer MCP Server exposes 8 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 Framer to LlamaIndex via MCP

Follow these steps to integrate the Framer 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 8 tools from Framer

Why Use LlamaIndex with the Framer MCP Server

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

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

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

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

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

Framer + LlamaIndex Use Cases

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

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

Data enrichment: query Framer 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 Framer for fresh data

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

Framer MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect Framer to LlamaIndex via MCP:

create_collection_item

Create a new CMS item

get_project

Get project details

get_site_info

Get site configuration

list_collection_items

List items in a CMS collection

list_collections

List CMS collections

list_pages

List all site pages

list_projects

List all Framer projects

publish_site

This makes changes visible to visitors. Publish the website

Example Prompts for Framer in LlamaIndex

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

"List all CMS collections in my Framer site."

"Add a new team member 'Ana Silva' to the Team Members collection."

"Publish my Framer site with the latest CMS changes."

Troubleshooting Framer MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Framer + LlamaIndex FAQ

Common questions about integrating Framer 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 Framer tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.