Can I search for failed workflows across my entire history?

Yes. The search tool supports Elasticsearch query syntax — search by status (FAILED, TIMED_OUT), workflow type, date ranges, or correlation IDs. Ask your agent 'show me all failed workflows from the last 24 hours' and it returns matching executions with their IDs, failure reasons, and timestamps.

What's the difference between workflow definitions and running instances?

Definitions are the blueprints — the graph schema with tasks, operators, and branching logic. Running instances are actual executions of those definitions, each with their own input data, current state, and task-by-task progress. Think of definitions as classes and instances as objects.

Does this integration support triggering new workflow executions?

Currently, this integration focuses on observability — listing definitions, monitoring running instances, and searching execution history. It does not trigger new workflow executions. For launching workflows, use the Orkes Conductor UI or direct API calls.

Orkes Conductor MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Name: Orkes Conductor
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Orkes Conductor through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Orkes Conductor "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Orkes Conductor?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Orkes Conductor MCP Server

Connect your Orkes Conductor cluster to any AI agent and get full visibility into your workflow orchestration layer — definitions, running instances, task states, and execution history.

Pydantic AI validates every Orkes Conductor tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Workflow Definitions — List all registered workflow definitions with versions and descriptions, or inspect a specific workflow's graph schema with tasks, operators, and branching logic
Task Definitions — List all registered task definitions available for orchestration within your workflows
Running Instances — List actively running workflow instances filtered by workflow name to monitor what's currently executing
Execution Details — Get deep state details for any workflow execution including input/output mappings, task-by-task trace histories, and exceptions
Workflow Search — Search across all workflow executions using Elasticsearch queries, filtering by status, correlation ID, or workflow type

The Orkes Conductor MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Orkes Conductor to Pydantic AI via MCP

Follow these steps to integrate the Orkes Conductor MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Orkes Conductor with type-safe schemas

Why Use Pydantic AI with the Orkes Conductor MCP Server

Pydantic AI provides unique advantages when paired with Orkes Conductor through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Orkes Conductor integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Orkes Conductor connection logic from agent behavior for testable, maintainable code

Orkes Conductor + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Orkes Conductor MCP Server delivers measurable value.

Type-safe data pipelines: query Orkes Conductor with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Orkes Conductor tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Orkes Conductor and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Orkes Conductor responses and write comprehensive agent tests

Orkes Conductor MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Orkes Conductor to Pydantic AI via MCP:

get_execution

Get deep state details of a specific Workflow Execution

get_workflow_def

Get a specific Workflow Definition explicitly by name

list_running

List active, running workflow instances by explicit workflow name

list_task_defs

List all explicitly registered Task Definitions via Conductor API

list_workflow_defs

List all registered overarching Workflow Definitions via Orkes API

search_workflows

Perform an elastic Search across all Workflow executions

Example Prompts for Orkes Conductor in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Orkes Conductor immediately.

"Show me all registered workflow definitions."

"Are there any failed workflows in the last 24 hours?"

"How many instances of the order-processing workflow are currently running?"

Troubleshooting Orkes Conductor MCP Server with Pydantic AI

Common issues when connecting Orkes Conductor to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Orkes Conductor + Pydantic AI FAQ

Common questions about integrating Orkes Conductor MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Orkes Conductor MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.