How to Use the Image-Charts MCP in Pydantic AI

Q: What happens if my chart data exceeds the 2KB limit on the MCP Server?

Your agent should be instructed to switch to renderchartpost. The framework will validate the larger payload and return the chart as a Base64 string.

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Connect Image-Charts MCP to Pydantic AI

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Setup Image-Charts with Pydantic AI

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Validate your chart parameters at runtime

The `generate_chart_url` tool guarantees that your generated chart parameters match the expected schema before the request ever leaves Pydantic AI. This runtime verification catches malformed data points or missing Image-Charts labels, preventing broken image links. If your Pydantic AI agent tries to pass a string where an Image-Charts number belongs, the framework raises a validation error immediately. This keeps your data pipelines clean and prevents silent failures in production.

Get type-safe Base64 images with Pydantic AI

The `render_chart_post` tool returns a structured Base64 image payload that fits perfectly into your Pydantic AI agent's strict output models. This is ideal for handling large telemetry datasets that exceed the 2KB URL limit inside your Image-Charts pipeline. Because the Image-Charts response is explicitly typed, your Pydantic AI agent can confidently route the Base64 string to other services. You don't have to worry about loose dictionaries or untyped API responses breaking your downstream Python code.

Connect the Image-Charts server in one line

The `generate_chart_url` tool becomes instantly available to your agent when you register the Image-Charts MCP Server using the unified `MCPToolset` class in Pydantic AI. This setup bypasses deprecated HTTP classes and keeps your codebase modern. The Pydantic AI framework handles the underlying MCP transport layer automatically. Your agent can immediately begin generating Image-Charts bar charts, line graphs, and QR codes without any manual API integration.

Setup guide

Set up Image-Charts MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "image-charts-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Image-Charts tools.",
)

result = await agent.run("List recent Image-Charts transactions")
print(result.output)

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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 Pydantic AI

Use the toolset constructor with the Vinkius MCP Server HTTP endpoint. Pass this toolset directly into your Agent constructor to give your model instant access to the chart tools.

Yes, the framework validates all arguments against the tool schemas before execution. This ensures that your inputs to `generate_chart_url` are formatted correctly every time.

Yes, because the framework is model-agnostic. You can run a local model and it will still use `render_chart_post` to generate charts, provided it can output the correct JSON parameters.

Your agent should be instructed to switch to `render_chart_post`. The framework will validate the larger payload and return the chart as a Base64 string.

Your validated application telemetry is processed entirely in memory to generate the image file and is never written to persistent storage. Vinkius runs the MCP Server in an isolated V8 sandbox, maintaining strict data isolation.

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