How to Use the SketricGen MCP in LlamaIndex

Q: Can I query past system interactions with SketricGen and LlamaIndex?

Yes. By indexing results from listconversations, you create a searchable memory for your application. Your agent can answer, 'What did we talk about last month?' based on actual recorded data.

Q: What if I need to manage multiple sources in SketricGen and LlamaIndex?

You can use listknowledgebases to identify all available repositories. The MCP Server supports connecting those distinct data silos into one unified, searchable index.

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Connect SketricGen MCP to LlamaIndex

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Setup SketricGen with LlamaIndex

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Indexing Live API Data

The best part is using `get_trace` output to index results. Instead of just printing a trace, LlamaIndex turns that raw execution data into searchable knowledge. You can then query past system behavior and get answers grounded in real API history. This capability makes your RAG applications way smarter. The data isn't hallucinated; it comes straight from the `get_trace` records.

Building Knowledge Bases

You can populate and manage knowledge through `list_knowledge_bases`. LlamaIndex treats this server as a source of structured truth. You can feed documentation or API outputs into the index, making all your data retrievable via semantic search. This structure allows you to build highly specific applications that answer questions based on your entire corporate data set.

Data Retrieval and Listing

When you need context, use `list_workflows` or `get_workflow`. These tools provide the schemas necessary to build effective retrieval pipelines. You can then combine workflow definitions with document chunks in a unified search index. It's about combining structured system knowledge with unstructured documents for maximum query power.

Setup guide

Set up SketricGen 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 SketricGen 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 SketricGen tools.",
)
response = await agent.run("List recent SketricGen data")

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Common questions about SketricGen MCP in LlamaIndex

LlamaIndex indexes the results from SketricGen's tools. This means that when a user asks a question, the answer is pulled directly from verifiable API data like `get_trace` records or contact information.

Yes. By indexing results from `list_conversations`, you create a searchable memory for your application. Your agent can answer, 'What did we talk about last month?' based on actual recorded data.

The server manages knowledge base content, conversation histories, agent details, and contact records. These structured data points become your searchable index.

You can use `list_knowledge_bases` to identify all available repositories. The MCP Server supports connecting those distinct data silos into one unified, searchable index.

Yes, it touches contact details and conversation records. Always verify your indexing scope to ensure you're only making private data available for search.

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