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* Parse structuredContent from MCP CallToolResult (#3313) The _parse_tool_result_from_mcp method only iterated over the content field from CallToolResult, ignoring the structuredContent field entirely. MCP servers that return JSON data via structuredContent (e.g., Power BI MCP) appeared to return None. Add handling for structuredContent: when present, serialize it as JSON text and append it to the result list. This preserves the data for the LLM while maintaining backward compatibility with existing behavior. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Parse MCP CallToolResult.structuredContent field to prevent tool results returning None Fixes #3313 * Address review feedback: add default=str to json.dumps and remove .checkpoints/ - Add default=str to json.dumps for structuredContent serialization so non-JSON-serializable values (e.g. bytes) degrade gracefully instead of raising TypeError - Remove all .checkpoints/ runtime artifacts from the repository - Add **/.checkpoints/ to .gitignore to prevent future accidental commits - Add test for non-serializable structuredContent values Fixes #3313 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #3313: Python: MCP CallToolResult.structuredContent field is not parsed, causing tool results to return None --------- Co-authored-by: Copilot <copilot@github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Harness Agent Samples
This folder demonstrates create_harness_agent โ a factory function that builds a
pre-configured, batteries-included agent by assembling the full agent pipeline
from a chat client.
What is create_harness_agent?
create_harness_agent bundles the following features into a single Agent instance:
| Feature | Description |
|---|---|
| Function invocation | Automatic tool calling loop |
| Per-service-call persistence | History persisted after every model call |
| Compaction | Context-window management (sliding window + tool result compaction) |
| TodoProvider | Todo list management for planning and tracking |
| AgentModeProvider | Plan/execute mode tracking |
| MemoryContextProvider | File-based durable memory (when memory_store provided) |
| SkillsProvider | File-based skill discovery and progressive loading |
| OpenTelemetry | Built-in observability |
Each feature can be disabled or customized via keyword arguments.
Samples
| File | Description |
|---|---|
harness_research.py |
Interactive research assistant with web search and planning workflow |
Running
# Set your Foundry environment variables
export FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project-name"
export FOUNDRY_MODEL="your-model-deployment-name"
# Authenticate with Azure (required for AzureCliCredential)
az login
# Run the research sample
python samples/02-agents/harness/harness_research.py
Key Concepts
Minimal Setup
create_harness_agent requires only a chat client and token budget parameters:
from agent_framework import create_harness_agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
agent = create_harness_agent(
client=FoundryChatClient(credential=AzureCliCredential()),
max_context_window_tokens=128_000,
max_output_tokens=16_384,
)
Customization
Disable or customize any feature:
agent = create_harness_agent(
client=client,
max_context_window_tokens=128_000,
max_output_tokens=16_384,
name="my-agent",
agent_instructions="Custom instructions here.",
disable_todo=True, # Skip todo management
disable_mode=True, # Skip plan/execute modes
disable_compaction=True, # Skip compaction
)
Plan/Execute Workflow
The AgentModeProvider enables a two-phase workflow:
- Plan mode โ Interactive: the agent asks questions, creates todos, gets approval
- Execute mode โ Autonomous: the agent works through todos independently