How to Use the Concord (Workflow Orchestration) MCP in Pydantic AI

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Connect Concord (Workflow Orchestration) MCP to Pydantic AI

Create your Vinkius account to connect Concord (Workflow Orchestration) to Pydantic AI 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 Concord (Workflow Orchestration) with Pydantic AI

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Perplexity

Validated Concord Tasks in Pydantic AI

Every tool response from `list_processes` or `get_project_details` is checked against Pydantic models. If the server returns garbage, your agent stops immediately. This prevents silent failures in your pipeline. You get the reliability of strict typing while your agent manages your deployment workflows.

Strictly Typed Process Control

Trigger jobs with `start_process` and rely on the framework to validate the inputs. It ensures your agent only sends commands that match your expected schema. Your Pydantic AI logic stays clean and predictable. You avoid the common trap of hallucinated arguments when managing your Concord instance.

Real-Time Pipeline Inspection

Use `list_running_processes` to see exactly what is executing. The agent receives a structured list that it uses to inform its next move. Because the response is validated, the agent handles the data with total accuracy. It makes managing complex CI/CD tasks much safer and more transparent.

Setup guide

Set up Concord (Workflow Orchestration) 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": {
        "concord-workflow-orchestration-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Concord (Workflow Orchestration) transactions")
print(result.output)

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Common questions about Concord (Workflow Orchestration) MCP in Pydantic AI

The framework matches the raw JSON from the server against your predefined Pydantic models. If the data doesn't fit, the agent raises a validation error instead of guessing.

The server runs externally and connects via SSE or Streamable HTTP. This keeps your agent runtime decoupled from the infrastructure it manages.

Yes, you can iterate through projects using the list tools and then apply specific logic to each one. The type-safety ensures you handle each project's metadata correctly.

Yes, the architecture is fully asynchronous. It handles the I/O for tool calls without blocking your agent's reasoning loop.

Your private process data is only handled during the agent's active session. We rely on your endpoint token to authenticate, ensuring that no unauthorized entity touches your logs or project records.

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