How does the AI know the correct JSON format for new entries?

The agent calls `list_content_types` first to read each collection's field names and types. It then builds a compliant JSON payload matching the schema before calling `create_entry`.

Can the agent delete content types or schemas?

No. `delete_entry` only removes individual data rows within a collection. It cannot alter, drop, or modify content type schemas or structural CMS settings.

Can I upload media files through the integration?

Yes. Use `upload_media_asset` to add images, videos, or documents to the Strapi Media Library. You can also browse existing assets with `list_assets`.

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

Name: Strapi
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 Strapi 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

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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 Strapi. "
            "You have 9 tools available."
        ),
    )

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

Integrate the robust headless architecture of Strapi seamlessly into your conversational LLM workflows. By linking your AI securely to the Strapi REST ecosystem, engineering and content teams can effortlessly design schema types, interact with entries, and orchestrate media libraries directly from the terminal.

LlamaIndex agents combine Strapi 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

Architecture Discovery — Quickly evaluate top-level content structures invoking list_content_types and systematically paginate underlying rows executing list_entries.
Content Construction — Drive agile content updates creating new JSON-formatted parameters natively by calling create_entry or updating existing rows via update_entry.
Asset Orchestration — Monitor uploaded visual data traversing the Media Library securely with list_assets or uploading remote dependencies instantly using upload_media_asset.
Audit & Clearance — Protect production integrity by securely tracking and listing authorized active members leveraging list_cms_users.

The Strapi 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 Strapi to LlamaIndex via MCP

Follow these steps to integrate the Strapi 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 Strapi

Why Use LlamaIndex with the Strapi MCP Server

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

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

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

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

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

Strapi + LlamaIndex Use Cases

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

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

Data enrichment: query Strapi 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 Strapi for fresh data

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

Strapi MCP Tools for LlamaIndex (9)

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

create_entry

Provide the plural ID and a JSON string of fields. Creates a new entry for a specific content type

delete_entry

This action is irreversible. Permanently deletes a content entry

get_entry_details

Retrieves details for a specific content entry

list_assets

Lists media assets stored in the Strapi Media Library

list_cms_users

Lists all registered CMS users

list_content_types

Lists all content types (collections and single types) defined in Strapi

list_entries

Provide the plural ID of the content type (e.g., "articles"). Lists entries for a specific content type

update_entry

Provide the plural ID, entry ID, and field updates. Updates fields of an existing content entry

upload_media_asset

Provide the public file URL to be fetched and uploaded. Uploads a new file to the Media Library

Example Prompts for Strapi in LlamaIndex

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

"Review my Strapi content types and show the schema for 'product'."

"Construct a newly formatted post about system updates in the 'articles' content type."

"Upload a new promotional image dependency securely into the Media Library."

Troubleshooting Strapi MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Strapi + LlamaIndex FAQ

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