Can my AI read the strict structure of a custom model before creating a block?

Yes! We highly recommend asking your agent to use `get_model_schema` first. By retrieving the exact JSON fields (Strings, Lists, Booleans), the AI naturally learns the shape of your `Page` or `HeroContent` model and formats its `create_visual_block` mutation flawlessly avoiding validation errors.

How can I count how many blogs or pages are in my Builder space?

You can use the native `count_model_entities` tool. By querying a model like `blog-post`, the agent accesses the Builder backend with `limit=1` exclusively to extract the metadata counts without downloading heavyweight content arrays—highly efficient for broad analytics.

Can the agent wipe an accidentally created page completely?

Yes. If an error occurs or a staging draft needs removal, the agent can call `wipe_visual_block`. Supply the targeted entity ID, and the HTTP DELETE command is irreversibly broadcasted to the Builder environment. Always review IDs manually.

Builder MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Builder
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Builder 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 Builder. "
            "You have 10 tools available."
        ),
    )

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

Connect your Builder.io space to any AI agent and take full programmatic control over your headless CMS architecture and visual pages through natural conversation.

LlamaIndex agents combine Builder 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 Automation — Create, update, or wipe specific content entries across any data model dynamically
Schema Navigation — List your active Builder models and analyze exact field definitions and strict JSON bounds
Search & Retrieval — Use query strings to pull matched content documents or count entities effortlessly
Media Fetching — Inspect metadata and URLs of uploaded assets living on the Builder platform
Headless Maintenance — Delete deprecated components or page sections instantly using targeted endpoints

The Builder 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 Builder to LlamaIndex via MCP

Follow these steps to integrate the Builder 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 Builder

Why Use LlamaIndex with the Builder MCP Server

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

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

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

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

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

Builder + LlamaIndex Use Cases

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

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

Data enrichment: query Builder 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 Builder for fresh data

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

Builder MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Builder to LlamaIndex via MCP:

count_model_entities

Quickly count the number of live items stored within a specific model

create_visual_block

Create new content entries or visual blocks inside a Builder model

get_media_file

Retrieve details about an uploaded media asset within Builder.io

get_model_schema

Get the exact field structure and schema definitions for a single model

get_single_content

g. `query.data.title=Home`). Retrieve a specific content document by query matching from Builder.io

list_builder_models

List all defined data models and schemas in the Builder space

list_model_content

Useful for fetching dynamic content blocks or pages. Retrieve a list of content entries for a specific Builder.io model

search_active_models

Find Builder models matching a specific criteria or substring

update_visual_block

Update an existing Builder.io content block or document

wipe_visual_block

Permanently delete a specific content entry from Builder.io

Example Prompts for Builder in LlamaIndex

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

"List all active Builder models in my workspace."

"Fetch the schema for the 'custom-hero' model."

"Find a page with the title "Home" on the 'page' model."

Troubleshooting Builder MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Builder + LlamaIndex FAQ

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