How to Use the Uploadcare MCP in LlamaIndex

Q: What happens if I try to list files with LlamaIndex?

The listfiles tool output provides raw file paths and basic metadata. When indexed, this allows you to search for files based on criteria like 'show me all videos uploaded last month,' pulling the data into your knowledge base.

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Connect Uploadcare MCP to LlamaIndex

Create your Vinkius account to connect Uploadcare 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 Uploadcare with LlamaIndex

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Indexing file metadata for LlamaIndex

The `get_file_details` tool allows you to retrieve technical metadata, which becomes a searchable knowledge source. Instead of just calling the API and losing the data, LlamaIndex indexes this output into your vector store. You can query past sessions about specific assets; for instance, asking 'What was the size of the image I uploaded last month?' The answer is grounded in the actual metadata retrieved by `get_file_details`.

Managing file collections via MCP Server

LlamaIndex can build knowledge around immutable file collections using `list_file_groups`. This means you can ask questions like, 'What files belong to the Q3 marketing group?' and the index answers using real data. This capability is perfect for RAG applications where context comes from structured groups. The tool output feeds directly into your unified, queryable knowledge base.

Handling file lifecycle with LlamaIndex

When you mark a temporary asset as permanent using `store_file`, that action's confirmation data can be indexed. This allows the user to later query the system and confirm, 'Did I successfully store the raw video files last week?' This provides an audit trail in your knowledge base—not just calling the tool, but remembering the outcome of the call.

Setup guide

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

LlamaIndex takes the structured output from Uploadcare tools (like `get_file_details`) and embeds it into a vector store. When you query, the system finds semantic matches against this stored API data, not just keyword hits.

The `list_files` tool output provides raw file paths and basic metadata. When indexed, this allows you to search for files based on criteria like 'show me all videos uploaded last month,' pulling the data into your knowledge base.

Absolutely. You can create a RAG application that combines regulatory documents with live API data. For example, checking if all files in a specific group (`list_file_groups`) have the necessary metadata to meet compliance standards.

The server touches project-level usage statistics and file group information. Specifically, `get_project_info` provides metrics on your overall account usage, which is indexed into the knowledge base.

While powerful, the index only knows what you explicitly feed it. If you run `batch_delete_files`, the success/failure confirmation needs to be manually captured and indexed for the knowledge base to remember that action.

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