How to Use the Yodiz MCP in LlamaIndex

Q: How do I get project context using Yodiz and LlamaIndex?

You call listprojects first. This output is indexed into your vector store, meaning you can query not just the names but also any metadata associated with those projects for deep search.

Q: Can I use Yodiz and LlamaIndex to retrieve user details?

Yes. When listusers runs, that data gets indexed. You can then ask questions like 'who was the lead developer on Project X' and get a grounded answer from your knowledge index.

Q: What is the best way to search bug history with Yodiz and LlamaIndex?

Running listbugs feeds all current issues into the index. You can then query for patterns—for instance, 'all bugs related to API calls'—and retrieve historical records semantically.

Q: Does the MCP Server handle complex project structures with Yodiz and LlamaIndex?

It does. By indexing listepics and listsprints, you create a relationship map. You can query how sprints related to epics in specific projects, making multi-layered searches straightforward.

Index Yodiz project data into a searchable knowledge base with LlamaIndex.

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Connect Yodiz MCP to LlamaIndex

Create your Vinkius account to connect Yodiz 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 Yodiz with LlamaIndex

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Creating Project Overviews

You can build an index of every project using `list_projects`. When the output is indexed, your RAG application allows you to semantically search past discussions about specific Yodiz projects. Instead of just seeing a list, you query the concept—like 'what was decided about Project Alpha'—and get answers grounded in the actual API data.

Deep Dive into Features and Stories

Indexing is powerful for deep dives. If you run `list_epics` or `list_user_stories`, those structured results become searchable knowledge. You can then query 'what stories were attached to the login epic' months later. This makes retrieving historical context reliable, avoiding hallucinations entirely.

Tracking Team and Issues

Need to know who worked on what? Indexing the output of `list_users` lets you search for user roles or names across multiple projects. Furthermore, running `list_bugs` makes all current issues searchable within your knowledge base. The LlamaIndex setup combines these live API data points with documents for a unified index.

Setup guide

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

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Common questions about Yodiz MCP in LlamaIndex

You call `list_projects` first. This output is indexed into your vector store, meaning you can query not just the names but also any metadata associated with those projects for deep search.

Yes. When `list_users` runs, that data gets indexed. You can then ask questions like 'who was the lead developer on Project X' and get a grounded answer from your knowledge index.

Running `list_bugs` feeds all current issues into the index. You can then query for patterns—for instance, 'all bugs related to API calls'—and retrieve historical records semantically.

It does. By indexing `list_epics` and `list_sprints`, you create a relationship map. You can query how sprints related to epics in specific projects, making multi-layered searches straightforward.

This server touches user account information and bug reports. Specifically, the `list_users` tool retrieves names and identifiers for all registered users in the workspace.

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