How do I correctly generate a Contentstack Delivery Token?

Log into your core Contentstack account dashboard securely. Access the specific Stack you wish to query and navigate toward the technical 'Settings' header before clicking on 'Tokens'. Within this specific submenu, specifically select 'Delivery Tokens'. Generate a new one with an identifying name, assigning it distinctly to your intended 'Environment'. Note down the produced Token safely to input along with the matching Stack API key below.

Can the agent update entries or publish content directly to the CMS?

No. The integration exclusively relies upon Contentstack's globally distributed 'Delivery API' rather than their 'Management API', deliberately restricting operations to a secure read-only modality focused on retrieving already published entries, preventing accidental automated or unauthorized payload mutations from impacting live production data stores.

Do I need to type the exact environment name as it appears in Contentstack?

Yes, accuracy is strictly required. Providing the exact environment name (e.g., 'production' or 'staging') matching the internal naming correctly ensures the endpoint filters accurately out published iterations of records belonging only to those targeted distribution layers you are aiming specifically at querying reliably during chat prompts.

Contentstack MCP Server for LlamaIndex 9 tools — connect in under 2 minutes

Name: Contentstack
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 9 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Contentstack 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 Contentstack. "
            "You have 9 tools available."
        ),
    )

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

Empower your conversational AI with secure and instant read access to your Contentstack headless CMS. Utilizing the Contentstack Delivery API, your agent can efficiently fetch published entries, retrieve asset URLs, and audit content type schema structures in real-time.

LlamaIndex agents combine Contentstack tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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

Entry Retrieval — Instruct the agent to query and read live content entries by searching for specific title tags or matching query filters.
Asset Discovery — Request exact URLs from the media library to find specific images, PDFs, or files needed in your conversational context.
Schema Inspections — Ask for a detailed structural breakdown of any Content Type before utilizing it in an external application.

The Contentstack MCP Server exposes 9 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 Contentstack to LlamaIndex via MCP

Follow these steps to integrate the Contentstack 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 9 tools from Contentstack

Why Use LlamaIndex with the Contentstack MCP Server

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

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

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

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

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

Contentstack + LlamaIndex Use Cases

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

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

Data enrichment: query Contentstack 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 Contentstack for fresh data

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

Contentstack MCP Tools for LlamaIndex (9)

These 9 tools become available when you connect Contentstack to LlamaIndex via MCP:

get_asset_details

Get details for a specific asset

get_content_type_details

Get the schema for a specific content type

get_entry

Get detailed content for a specific entry

get_stack_summary

Get high-level metadata about the current stack

list_assets

List all published assets

list_content_types

List all content types in the stack

list_entries

List published entries for a specific content type

search_entries

Search for entries using a JSON query

sync_content

Retrieve delta of changes since last sync

Example Prompts for Contentstack in LlamaIndex

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

"Retrieve the published blog post entry with the title 'Future Trends in AI' from our primary environment."

"Describe the content model schema required for 'Hero Banner' items in my stack."

"List the most recent image assets uploaded to our Contentstack library."

Troubleshooting Contentstack MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Contentstack + LlamaIndex FAQ

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