How to Use the Mockaroo MCP in LlamaIndex

Q: How do I index Mockaroo schemas into LlamaIndex?

Use listschemas to pull your templates, then convert the text output into document nodes for your vector index. Your agent can then search them semantically.

Q: Can LlamaIndex generate data using Mockaroo on the fly?

Yes, your agent calls generatemockdata during query execution to produce test inputs based on user prompts.

Q: How does Mockaroo handle relational data inside LlamaIndex?

Your agent uses listdatasets to find primary key references, then builds matching foreign key records using generatefromschema.

Q: How do I check MCP Server field formats?

Call listfieldtypes from your LlamaIndex agent to get the exact strings required for synthetic data generation.

Index synthetic Mockaroo datasets directly into your LlamaIndex vector store for realistic RAG testing.

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Setup Mockaroo with LlamaIndex

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Grounding LlamaIndex RAG pipelines with real schemas

The `list_schemas` tool lets your LlamaIndex agent fetch and index your Mockaroo templates via MCP Server. Your agent queries this LlamaIndex vector store to find the right Mockaroo data structure before writing test assertions. Using this indexed Mockaroo schema metadata prevents LlamaIndex hallucinations during automated test generation. Reading the actual Mockaroo schemas instead of guessing field names gives your LlamaIndex agent an exact map of your database layout.

Semantic search over Mockaroo field types

Executing `list_field_types` pulls hundreds of supported data formats into your LlamaIndex knowledge base. Your LlamaIndex agent performs semantic searches over these types to select the perfect Mockaroo generator for custom user profiles or financial transactions. Once the LlamaIndex agent identifies the correct format, it calls `generate_mock_data` to construct the Mockaroo record. This index-driven LlamaIndex selection ensures highly accurate Mockaroo data without manual mapping.

Loading external datasets into vector indexes

The `list_datasets` tool exposes your uploaded reference files directly to your LlamaIndex MCP Server pipeline. Your LlamaIndex agent reads these reference sets, indexes their contents, and uses them to guide Mockaroo generation. You can combine these indexed datasets with `generate_from_schema` to produce highly realistic relational Mockaroo data inside LlamaIndex. The resulting Mockaroo output matches the statistical distribution of your real-world reference files within the LlamaIndex retriever.

Setup guide

Set up Mockaroo MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Mockaroo MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Mockaroo tools.",
)
response = await agent.run("List recent Mockaroo data")

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Common questions about Mockaroo MCP in LlamaIndex

Use `list_schemas` to pull your templates, then convert the text output into document nodes for your vector index. Your agent can then search them semantically.

Yes, your agent calls `generate_mock_data` during query execution to produce test inputs based on user prompts.

Your agent uses `list_datasets` to find primary key references, then builds matching foreign key records using `generate_from_schema`.

Call `list_field_types` from your LlamaIndex agent to get the exact strings required for synthetic data generation.

Your raw schemas are processed entirely inside the V8 sandbox before transmission. Only the final synthetic records are indexed into your LlamaIndex vector store, preventing exposure of proprietary database metadata.

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