MCP Pipeline Orchestrator FAQs

Question 1

Can I customize the pipeline for my own project?

Accepted Answer

Yes, you can define your own steps as async functions by following the patterns in the included research_implement_pipeline.py script and utilizing the PipelineContext for shared state.

Question 2

How are environment variables handled for API keys?

Accepted Answer

The skill includes a critical fix for the MCP SDK that ensures the full os.environ is passed to subprocesses, allowing API keys stored in your local environment to reach the MCP servers.

Question 3

Is there a way to test the pipeline safely?

Accepted Answer

Yes, the skill includes a --dry-run flag to preview the execution plan and a dedicated test harness that uses an isolated sandbox directory to prevent accidental changes to your main codebase.

Question 4

How does this skill handle missing or disabled tools?

Accepted Answer

It uses a graceful degradation pattern where each tool is checked for availability before execution. If a tool is disabled or lacks an API key, the pipeline skips that specific step and continues the workflow.

Question 5

What MCP tools are included in the default pipeline?

Accepted Answer

The pipeline chains nia for documentation search, ast-grep for structural code search, morph for fast codebase editing, qlty for code validation, and git for version control operations.

MCP Pipeline Orchestrator

Key Features

Use Cases

MCP Pipeline Orchestrator

Key Features

Use Cases