How to Use the SketricGen MCP in Pydantic AI

Q: How do I use SketricGen for workflows when using Pydantic AI?

You trigger a workflow via runworkflow. Because you're running this through Pydantic AI, the framework validates every piece of data passed between agents. This means your agent only processes correctly structured information.

Q: What kind of history can SketricGen provide when using Pydantic AI?

You track execution paths with listtraces and gettrace. The resulting data is immediately validated by the framework, meaning you get clean, reliable audit logs. You also monitor resource use with gettracecredits.

Q: How do I confirm SketricGen's status when using Pydantic AI?

Always start by calling checksketricgenstatus. This confirms that all tools on the MCP Server are active and ready for your agent to call.

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Setup SketricGen with Pydantic AI

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Execute Reliable Workflows

Run workflows using `run_workflow`. Because your system uses the Pydantic AI framework, every data handoff within this workflow is validated against a schema. If an agent passes bad data, you fail loud and early. This level of validation means you don't have to worry about silent corruption or hallucinated fields ruining your production runs. You get guaranteed type safety.

Track Agent Interactions

Need to know what agents exist? Use `list_agents` and `get_agent`. This confirms the full set of specialized tools available on the MCP Server for your agent to use. It helps you build reliable, modular systems. Want to check who needs talking to? The `list_contacts` tool lets you fetch contact lists before a workflow runs, ensuring all input data is structured correctly.

Manage System State

Keep track of your system's assets and history. You can list all available workflows (`list_workflows`) or get details on a specific one using `get_workflow`. This gives you an architectural view of what the MCP Server supports. Furthermore, you can manage context by listing templates with `list_templates`, ensuring any new process starts with the correct structure.

Setup guide

Set up SketricGen 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": {
        "sketricgen-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent SketricGen transactions")
print(result.output)

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Common questions about SketricGen MCP in Pydantic AI

You trigger a workflow via `run_workflow`. Because you're running this through Pydantic AI, the framework validates every piece of data passed between agents. This means your agent only processes correctly structured information.

You track execution paths with `list_traces` and `get_trace`. The resulting data is immediately validated by the framework, meaning you get clean, reliable audit logs. You also monitor resource use with `get_trace_credits`.

Yes. You interact with context by listing and retrieving specific `knowledge_bases`. This process ensures that the data provided to your agent is structured correctly before it's used for reasoning.

You can manage conversational context by listing chats with `list_conversations` or fetching details with `get_conversation`. If the chat needs wiping, you call `delete_conversation`.

Always start by calling `check_sketricgen_status`. This confirms that all tools on the MCP Server are active and ready for your agent to call.

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