mcp-chaining

# MCP Chaining Pipeline A research-to-implement pipeline that chains 5 MCP tools for end-to-end workflows. ## When to Use - Building multi-tool MCP pipelines - Understanding how to chain MCP calls with graceful degradation - Debugging MCP environment variable issues - Learning the tool naming conventions for different MCP servers ## What We Built A pipeline that chains these tools: | Step | Server | Tool ID | Purpose | |------|--------|---------|---------| | 1 | nia | `nia__search` | Search library documentation | | 2 | ast-grep | `ast-grep__find_code` | Find AST code patterns | | 3 | morph | `morph__warpgrep_codebase_search` | Fast codebase search | | 4 | qlty | `qlty__qlty_check` | Code quality validation | | 5 | git | `git__git_status` | Git operations | ## Key Files - `scripts/research_implement_pipeline.py` - Main pipeline implementation - `scripts/test_research_pipeline.py` - Test harness with isolated sandbox - `workspace/pipeline-test/sample_code.py` - Test sample code ## Usage Examples ```bash # Dry-run pipeline (preview plan without changes) uv run python -m runtime.harness scripts/research_implement_pipeline.py \ --topic "async error handling python" \ --target-dir "./workspace/pipeline-test" \ --dry-run --verbose # Run tests uv run python -m runtime.harness scripts/test_research_pipeline.py --test all # View the pipeline script cat scripts/research_implement_pipeline.py ``` ## Critical Fix: Environment Variables The MCP SDK's `get_default...