Flotiq 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 Flotiq 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 Flotiq. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Flotiq?"
)
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 Flotiq MCP Server
Connect your Flotiq account to any AI agent and take full control of your API-first headless CMS and structured content delivery through natural conversation.
LlamaIndex agents combine Flotiq 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
- Content Object Orchestration — Identify bounded routing spaces inside the headless Flotiq CMS and extract explicitly attached REST arrays targeting specific content types natively
- Live Record Management — Provision highly-available JSON payloads to write or update Flotiq models, or irreversibly vaporize specific nodes to clear live database bytes
- Schema Auditing — Retrieve the exact structural matching for delivery models and enumerate explicitly attached structured rules exporting active type vectors
- Global Semantic Search — Execute immediate queries across all content by tapping raw status configurations validating words bounding Elastic/Graph limits flawlessly
- Media Asset Discovery — Perform structural extraction of properties driving active media limits by hitting physical CDN uploads mapped in your tenant environment
- Relational Data Hydration — Analyze specific ID configurations mapping to internal dependencies and parsing relations securely through hydrated object retrieval
- Tenant Oversight — Identify precise active arrays spanning your rented identity limits, analyzing quotas and base endpoints available synchronously
The Flotiq 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 Flotiq to LlamaIndex via MCP
Follow these steps to integrate the Flotiq 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 Flotiq
Why Use LlamaIndex with the Flotiq MCP Server
LlamaIndex provides unique advantages when paired with Flotiq through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Flotiq tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Flotiq tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Flotiq, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Flotiq tools were called, what data was returned, and how it influenced the final answer
Flotiq + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Flotiq MCP Server delivers measurable value.
Hybrid search: combine Flotiq real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Flotiq 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 Flotiq for fresh data
Analytical workflows: chain Flotiq queries with LlamaIndex's data connectors to build multi-source analytical reports
Flotiq MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Flotiq to LlamaIndex via MCP:
create_cms_object
Provision a highly-available JSON Payload writing models natively
get_content_details
Retrieve explicit Cloud logging tracing explicit Payload IDs limitlessly
get_content_type_schema
Retrieve the exact structural matching verifying Delivery Model blocks
get_tenant_limits
Identify precise active arrays spanning rented Identity limits
list_all_content_types
Enumerate explicitly attached structured rules exporting active Type vectors
list_content_objects
Identify bounded routing spaces inside the Headless Flotiq CMS
list_media_assets
Perform structural extraction of properties driving active Media limits
patch_cms_object
Mutate global Web CRM boundaries substituting Attributes safely
search_global_content
Inspect deep internal arrays mitigating specific Picture constraints
wipe_cms_object
Irreversibly vaporize explicit App nodes dropping live Database bytes
Example Prompts for Flotiq in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Flotiq immediately.
"List all items of content type 'blogpost'"
"Show me the JSON schema for content type 'product'"
"Search global content for 'feature launch'"
Troubleshooting Flotiq MCP Server with LlamaIndex
Common issues when connecting Flotiq to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFlotiq + LlamaIndex FAQ
Common questions about integrating Flotiq 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 Flotiq 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 Flotiq to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.