Amplience 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 Amplience as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Amplience. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Amplience?"
)
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 Amplience MCP Server
Link your Amplience headless CMS to any intelligent AI agent to completely rethink how you handle your enterprise content architecture, deploying components natively through standard conversation.
LlamaIndex agents combine Amplience 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
- Discover Asset Hierarchies — Freely list top-level Hubs, target specific Repositories, and fetch internal Folders to help your AI inherently understand where every graphic and article lives.
- Content Retrieval — Paginate through dynamic content items, safely extracting complete metadata alongside current active schemas and validation rules.
- Edit & Create Structure — Give the agent full permission to push correctly strictly-typed JSON payloads back into the system, generating or modifying blog entries and product metadata.
- Manage Deployments — Permanently execute deletions (if revision locks permit) or instruct the system to fire a specific content configuration directly over to the edge delivery API to hit the live website.
The Amplience 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 Amplience to LlamaIndex via MCP
Follow these steps to integrate the Amplience 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 Amplience
Why Use LlamaIndex with the Amplience MCP Server
LlamaIndex provides unique advantages when paired with Amplience through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Amplience tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Amplience tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Amplience, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Amplience tools were called, what data was returned, and how it influenced the final answer
Amplience + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Amplience MCP Server delivers measurable value.
Hybrid search: combine Amplience real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Amplience 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 Amplience for fresh data
Analytical workflows: chain Amplience queries with LlamaIndex's data connectors to build multi-source analytical reports
Amplience MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Amplience to LlamaIndex via MCP:
create_content_item
Create a new structured content item adhering to a schema inside a folder
delete_content_item
Requires version validation before deletion. Permanently delete a content item from the repository database
get_content_item
Retrieve a specific content item configuration and its schema revision lock
get_delivery_content
Retrieve the exact structural matching verifying Delivery CDN blocks
list_content_items
Retrieve paginated content items from a specific repository
list_folders
List all folders organizing content in a given repository
list_hubs
Essential for retrieving the active workspace. List all accessible Amplience Hubs (environments)
list_repositories
List all content repositories within a specific Hub
publish_content_item
Publish a specific content item version to the live delivery CDN
update_content_item
Update an existing content item data structure matching its current schema
Example Prompts for Amplience in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Amplience immediately.
"Identify all active repositories present inside my default Amplience Hub."
"Pull the structural metadata (schema lock and payload) of item '5tYv92'."
"Publish the newly edited Content '5tYv92' to the global live network."
Troubleshooting Amplience MCP Server with LlamaIndex
Common issues when connecting Amplience to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAmplience + LlamaIndex FAQ
Common questions about integrating Amplience 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 Amplience 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 Amplience to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.