How to Use the YepCode MCP in LlamaIndex

Q: How does LlamaIndex use YepCode's process tools?

LlamaIndex treats the outputs from MCP Server tools—like listprocesses or getprocessdetails—as source data. It ingests these structured results into a vector store for deep semantic querying.

Q: Can LlamaIndex query past YepCode runs?

YepCode exposes listexecutions. You can feed this history into your index, allowing the AI client to retrieve answers grounded in actual API run data, not just general knowledge.

Q: Is the MCP Server used with LlamaIndex stateless?

No. By indexing the results of tool calls like listteams, LlamaIndex creates a persistent, stateful knowledge base that survives individual query sessions.

Turn YepCode's live process data into a searchable knowledge base with LlamaIndex.

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Connect YepCode MCP to LlamaIndex

Create your Vinkius account to connect YepCode to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup YepCode with LlamaIndex

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Indexing Process History for Search

You can gather historical context by calling `list_executions` and then indexing that output. This turns raw run logs into structured data, allowing semantic search against your past activity. Later, when a user asks about old failures or successes, LlamaIndex queries the index rather than relying on memory limitations.

Building Knowledge from Team Structures

Use `list_teams` to gather organizational data. Indexing this information means you can ask complex questions like, 'Which teams interact with service X?' and get an answer grounded in your actual API structure. This capability is huge for compliance or onboarding, letting the AI client query facts instead of making guesses.

Querying Current Process Statuses

Want to know what's running right now? Call `get_process_details` and feed the output into your index. This provides immediate, verifiable data that can be combined with documentation. By indexing this live status, you create a single source of truth for both current operations and historical context.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by YepCode. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about YepCode MCP in LlamaIndex

LlamaIndex treats the outputs from MCP Server tools—like `list_processes` or `get_process_details`—as source data. It ingests these structured results into a vector store for deep semantic querying.

YepCode exposes `list_executions`. You can feed this history into your index, allowing the AI client to retrieve answers grounded in actual API run data, not just general knowledge.

The server provides process details, execution logs, and team membership information. These varied data types are indexed together so you can ask cross-domain questions.

No. By indexing the results of tool calls like `list_teams`, LlamaIndex creates a persistent, stateful knowledge base that survives individual query sessions.

This server touches account secrets. When querying historical data, always be mindful of the sensitive nature of secret credential details returned by `list_secrets`.

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