mcp-chaining

A research-to-implement pipeline that chains 5 MCP tools for end-to-end workflows. A pipeline that chains these tools: The MCP SDK's get_default_environment() only includes basic vars (PATH, HOME, etc.), NOT os.environ . We fixed src/runtime/mcp_client.py to pass full environment: This ensures API keys from ~/.claude/.env reach subprocesses. Each tool is optional. If unavailable (disabled, no API key, etc.), the pipeline continues: The pipeline captures errors without failing the entire run: - 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 - 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 - Copy the pattern from scripts/research_implement_pipeline.py - Define your steps as async functions - Use check_tool_available() for graceful degradation - Chain results through PipelineContext - Aggregate with print_summary()