How to Use the Met Museum MCP in LlamaIndex

Q: Can I index Met Museum search results directly into a vector store using LlamaIndex?

Yes, you can use searchobjects to find relevant IDs, pull their full metadata, and load them into a vector index. — and there it is — you now have semantic search capabilities over the retrieved museum records.

Q: How does LlamaIndex handle data updates from the Met Museum MCP Server?

You can set up a scheduled ingestion pipeline that calls listobjects to check for new IDs. Your LlamaIndex agent then updates the vector store with any new metadata fetched from the museum.

Q: Can I restrict the tools available to my LlamaIndex agent?

Yes, you can use the allowedtools filter when initializing your tool spec. This lets you expose only specific endpoints, like getobject, while keeping the indexing pipeline secure.

Index Met Museum artwork metadata directly into LlamaIndex vector stores for highly accurate, RAG-driven art history search.

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Connect Met Museum MCP to LlamaIndex

Create your Vinkius account to connect Met Museum 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 Met Museum with LlamaIndex

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Semantic Indexing of Met Museum Data with LlamaIndex

`get_object` pulls detailed metadata for specific artworks, which your pipeline immediately indexes into a vector store. By converting raw museum records into searchable document nodes, your agent answers complex historical questions without hallucinating. This MCP Server integration bridges the gap between live museum APIs and offline vector databases. Your LlamaIndex RAG application queries the indexed data, matching user prompts with actual curatorial records rather than relying on pre-trained weights.

Targeted Department Querying

`list_departments` allows your indexer to categorize incoming documents by their official museum departments. Your pipeline maps each department ID to its corresponding artwork metadata, creating structured indexes for faster retrieval. This structured approach improves semantic search accuracy. By filtering vector queries by department first, your LlamaIndex agent avoids searching irrelevant collections and delivers faster, more precise answers.

Bulk Object Ingestion

`list_objects` retrieves large batches of valid IDs to populate your local vector index. Your ingestion pipeline loops through these IDs, fetches the full metadata via `get_object`, and stores the resulting text nodes. This process turns a public REST API into a dynamic knowledge source. Your agent can search past ingestion sessions, combining live API data with local documents to build a deep art history search engine.

Setup guide

Set up Met Museum 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 Met Museum 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 Met Museum tools.",
)
response = await agent.run("List recent Met Museum 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 Met Museum. 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 Met Museum MCP in LlamaIndex

You install the llama-index-tools-mcp package, initialize the basic client with your server URL, and convert the tools using McpToolSpec. You can then pass these tools directly to your FunctionAgent.

Yes, you can use `search_objects` to find relevant IDs, pull their full metadata, and load them into a vector index. — and there it is — you now have semantic search capabilities over the retrieved museum records.

You can set up a scheduled ingestion pipeline that calls `list_objects` to check for new IDs. Your LlamaIndex agent then updates the vector store with any new metadata fetched from the museum.

Yes, you can use the allowed_tools filter when initializing your tool spec. This lets you expose only specific endpoints, like `get_object`, while keeping the indexing pipeline secure.

This MCP Server only processes public museum metadata, department listings, and object IDs. Vinkius hosts the server in a secure V8 isolate sandbox, ensuring your indexed data, vector store queries, and API tokens remain private.

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