MCP-enabled agents fail as second step in sequential YAML workflows with "Object/Array type mismatch" error

## Environment
- **azure-ai-projects SDK version**: >=2.0.0b1
- **Python version**: 3.x
- **Date observed**: January 2026

## Description
When invoking an agent with MCP tools as the second step in a declarative YAML workflow, the workflow fails with an error indicating a type mismatch between Object and Array.

## Error Message
```
Unhandled workflow failure - #validateuser_agent (InvokeAzureAgent) ->
The requested operation requires an element of type 'Object', but the target element has type 'Array'.
```

## Steps to Reproduce
1. Create a prompt agent without MCP tools (Agent A)
2. Create a prompt agent with MCP tools (Agent B)
3. Create a sequential YAML workflow that invokes Agent A, then Agent B
4. Run the workflow

## Expected Behavior
Both agents execute successfully in sequence.

## Actual Behavior
- Agent A completes successfully
- Agent B fails immediately with the type mismatch error
- The error occurs regardless of input format (text, messages, static text)

## Workaround Verified
Using the same agent (without MCP) twice in sequence works correctly, confirming the workflow syntax is valid.

## Minimal Reproduction Code
```python
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import WorkflowAgentDefinition
from azure.identity import DefaultAzureCredential

workflow_yaml = """
kind: workflow
trigger:
kind: OnConversationStart
id: sequential_workflow
actions:
- kind: CreateConversation
id: create_conversation
conversationId: Local.ConversationId

- kind: InvokeAzureAgent
id: agent_no_mcp
conversationId: "=Local.ConversationId"
agent:
name: AgentWithoutMCP
input:
text: "=System.LastMessageText"

- kind: InvokeAzureAgent
id: agent_with_mcp
conversationId: "=Local.ConversationId"
agent:
name: AgentWithMCPTools # <-- This fails
input:
text: "Process the above."

- kind: EndConversation
id: end
"""

# Create and run workflow
with DefaultAzureCredential() as credential:
with AIProjectClient(endpoint=endpoint, credential=credential) as client:
workflow = client.agents.create_version(
agent_name="test-workflow",
definition=WorkflowAgentDefinition(workflow=workflow_yaml),
)
# Run workflow - second agent fails
```

## Additional Context
- The MCP agent works correctly when invoked standalone
- The MCP agent works correctly when invoked as the first (and only) agent in a workflow
- The issue only occurs when MCP agent is invoked after another agent in a sequential workflow

Beginne mit der minimalen Reproduktion unter Verwendung von AIProjectClient, WorkflowAgentDefinition und create_version. Vergleiche den sequenziellen Workflow, wenn der MCP-Agent an erster Stelle bzw. an zweiter Stelle steht, und untersuche, wie InvokeAzureAgent die gemeinsame Konversation und Eingabe zwischen diesen Schritten verarbeitet. Als erledigt gilt die Aufgabe, wenn beide Agents nacheinander erfolgreich ausgeführt werden, ohne dass es zu einem Object/Array type mismatch kommt.

Vom Indexierungsmodell aus dem Issue-Text verfasst.