How to Use the Image-Charts MCP in LlamaIndex

Q: Can I query past Image-Charts configurations inside LlamaIndex?

Yes. By indexing the arguments sent to generatecharturl, your agent can search and reuse those exact configurations later.

Q: How does LlamaIndex handle the Base64 output from Image-Charts?

The renderchartpost tool returns a Base64 string that LlamaIndex can store as metadata inside your vector nodes.

Index live visual data and feed chart configurations directly into your LlamaIndex knowledge base with this MCP Server.

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Connect Image-Charts MCP to LlamaIndex

Create your Vinkius account to connect Image-Charts 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 Image-Charts with LlamaIndex

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Indexing Chart Parameters for RAG

The `generate_chart_url` tool allows LlamaIndex agents to create visual representations of query results instantly. The agent doesn't just show the chart; it can index the configuration parameters back into your vector store. This lets you query past data visualizations later. Your RAG pipeline can look up how a specific chart was formatted and replicate it without starting from scratch.

Base64 Storage in Vector Stores

The `render_chart_post` tool handles massive datasets by returning a Base64 image payload that LlamaIndex can store directly. LlamaIndex can store this raw data alongside your semantic indices for quick retrieval. This ensures your document indexes contain both the raw numbers and the actual generated visual assets. You get a complete, searchable record of every visualization your system creates.

Grounding Visual Data in LlamaIndex

The `generate_chart_url` tool ensures your LlamaIndex agent bases its charts on live data instead of hallucinating values. Using this MCP Server ensures the agent pulls raw numbers from your index and feeds them directly to the rendering engine. This setup prevents the agent from making up trends or rendering broken images. The resulting output is always grounded in the actual documents you indexed.

Setup guide

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

Yes. By indexing the arguments sent to `generate_chart_url`, your agent can search and reuse those exact configurations later.

The `render_chart_post` tool returns a Base64 string that LlamaIndex can store as metadata inside your vector nodes.

Absolutely. You can expose these tools to a specialized visualization agent while other agents handle data retrieval.

Yes, you can use the basic client tool spec to restrict or filter which tools are exposed to your agent.

Yes. Your raw index metrics are sent to the server solely to build the image, running in a zero-trust sandbox that discards data immediately.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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