How to Use the zipBoard MCP in LlamaIndex

Q: How do I search past zipBoard projects using LlamaIndex?

You run listprojects with the MCP Server, and then index that output. Later, you can ask a question about 'Project Alpha' and LlamaIndex uses vector search to ground the answer in the data you previously gathered.

Q: Can LlamaIndex help me recall zipBoard tasks?

Yes. After running listtasks, the results are indexed into your knowledge base. You can then query, 'What was the status of Task 123?' and get an answer derived from those stored project task details.

Q: Can I search through contents listed by zipBoard files using LlamaIndex?

You list the files first with listfiles, then index the metadata. You can build a query that asks about file content or location, and LlamaIndex will point you to the relevant indexed record.

Q: How do I use zipBoard data in my RAG application with LlamaIndex?

You first call getorganization via your AI client. This output is treated as a document chunk and added to the index alongside any other source documents, making it part of your knowledge base.

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Setup zipBoard with LlamaIndex

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Indexing Results for Semantic Search

LlamaIndex takes the output from any MCP tool call and indexes it into your vector store. For instance, when you run `list_projects`, that entire list of projects becomes searchable knowledge, not just a transient API response. This means later queries can ground answers in actual zipBoard data. This is huge for building RAG apps. You query past session results—say, 'What tasks did we create last month?'—and LlamaIndex searches the indexed records to give you an answer based on real `list_tasks` output.

Querying Historical zipBoard Data

Don't rely on memory. You can query historical data like organization details or project file lists using LlamaIndex. If you ran `get_organization` last week, LlamaIndex makes that data part of your searchable index. You ask a question and get the answer grounded in the API data. This capability lets developers build applications where live zipBoard API information is combined with unstructured documents into one unified knowledge base.

Combining Documents and MCP Server Tools

LlamaIndex treats your MCP tool calls as just another source of truth. You can combine a PDF manual with the output from `list_files`. Your agent reads the document *and* knows what files exist in zipBoard, letting it answer questions that bridge both domains. The system combines live API data with documents into one queryable index. This is how you move beyond simple tool execution and into true knowledge augmentation.

Setup guide

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

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Common questions about zipBoard MCP in LlamaIndex

You run `list_projects` with the MCP Server, and then index that output. Later, you can ask a question about 'Project Alpha' and LlamaIndex uses vector search to ground the answer in the data you previously gathered.

Yes. After running `list_tasks`, the results are indexed into your knowledge base. You can then query, 'What was the status of Task 123?' and get an answer derived from those stored project task details.

The server exposes structured data like project tasks, organization configuration, and file URLs. These specific types are ideal for indexing because they provide clear context and searchable fields.

You list the files first with `list_files`, then index the metadata. You can build a query that asks about file content or location, and LlamaIndex will point you to the relevant indexed record.

You first call `get_organization` via your AI client. This output is treated as a document chunk and added to the index alongside any other source documents, making it part of your knowledge base.

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