MCP Integration Scaffold Generator

You are tasked with generating the scaffolding required to integrate a new Model Context Protocol (MCP) server.

Execution Steps:

1. Gather Requirements: Ask the user for:

   • The name of the MCP server.
   • The command/executable required to run it (e.g. npx -y @modelcontextprotocol/server-postgres).
   • Any required environment variables (e.g. database URLs, API Keys).

2. Scaffold the Integration: Using bash file creation tools:

   • If this is going into a Claude Code environment, update the claude.json configuration file to include the new server definition under the mcpServers object.
   • Ensure you properly map any provided environment variables in the configuration.
   • Scaffold a CONNECTORS.md file alongside the integration. This file should map the MCP server's required tool targets to an abstract tag (e.g. mapping literature_search tool to the abstract tag ~~literature), ensuring that plugins remain portable and resilient against underlying MCP server swaps.
   • Create a basic testing script or prompt (perhaps leveraging create-skill) that the agent can use to test the new MCP tools once attached. Inform the testing scripts to utilize the abstract ~~tag rather than hardcoding the actual MCP tool namespace. Ensure this test workflow applies Conditional Step Inclusion (e.g., explicitly stating "If Connected" in the header) so it degrades gracefully rather than failing silently if the server isn't running.

3. Confirmation: Print a success message showing the modified configuration. Instruct the user that they may need to restart their agent environment to pick up the new MCP handles.

4. If Optimizing Trigger Behavior: Apply autoresearch-style governance:

   • Baseline-first eval.
   • One dominant change per loop.
   • Keep/discard decisions.
   • Crash/timeout logging.
   • Persisted iteration ledger in evals/results.tsv.

Next Actions

• Continuous Improvement: Run ./scripts/benchmarking/run_loop.py --results-dir evals/experiments for repeatable trigger calibration.
• Review Loop: Run ./scripts/eval-viewer/generate_review.py to inspect false positives/false negatives.
• Audit: Offer to run audit-plugin to validate the generated artifacts.