How to Use the Grid MCP in LlamaIndex

Q: How do I feed Grid worksheet rows into a LlamaIndex vector store?

You use the MCP tool spec to fetch rows via listworksheetrecords. LlamaIndex then parses these records into Document objects and indexes them for semantic retrieval.

Q: How does LlamaIndex know which Grid worksheets are available?

The agent calls listgridworksheets to get a clean list of all operational sheets. From there, it can drill down into specific sheets using getworksheetdetails.

Q: Can I filter which assets LlamaIndex indexes?

You can use listsupportedassettypes to filter out irrelevant data formats. This keeps your vector index clean and saves token usage during the embedding process.

Index live Grid worksheet records into your LlamaIndex vector store for instant semantic search across your operational dashboards.

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Setup Grid with LlamaIndex

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Indexing Grid records into LlamaIndex

Stop scrolling through endless rows to find a specific operational event. This MCP Server lets your LlamaIndex RAG pipeline pull raw data via `list_worksheet_records` and index those rows into a local vector store. When users query their dashboards, the engine searches the indexed embeddings of your spreadsheet. It retrieves the exact matching records without relying on slow, literal string searches.

Enriching LlamaIndex context with worksheet schemas

Knowing the raw data isn't enough; you need the structural context. Your indexer can run `get_worksheet_details` to extract columns, data types, and constraints before building nodes. This structural data allows LlamaIndex to build more accurate query engines. The engine maps user questions to specific columns, making your natural language dashboard queries incredibly precise.

Dynamic RAG updates from live Grid sources

Operational dashboards change by the minute. Your LlamaIndex ingestion pipeline can query `list_grid_dashboards` to discover new visualization targets dynamically. Once found, it pulls the fresh data, updates the index, and discards stale nodes. If the pipeline hits a snag, it runs `check_api_health` to verify connection status before retrying.

Setup guide

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

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Common questions about Grid MCP in LlamaIndex

You use the MCP tool spec to fetch rows via `list_worksheet_records`. LlamaIndex then parses these records into Document objects and indexes them for semantic retrieval.

Yes, the tool spec exposes write operations. Your agent can call `update_worksheet_record` or `create_new_operational_record` to modify rows based on user queries.

The agent calls `list_grid_worksheets` to get a clean list of all operational sheets. From there, it can drill down into specific sheets using `get_worksheet_details`.

You can use `list_supported_asset_types` to filter out irrelevant data formats. This keeps your vector index clean and saves token usage during the embedding process.

Your cell values and visual dashboard layouts are strictly processed in-memory. The Vinkius MCP Server sandbox ensures that no spreadsheet data is cached or leaked during the indexing pipeline.

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