Real-time structural Code Health via CodeScene MCP — review before edits, verify score deltas after changes, gate commits and PRs. Use when reviewing code quality, refactoring, checking if AI changes degraded a file, or before commit/PR.

Code Health MCP (CodeScene) Structural maintainability feedback for AI assisted coding. Complements style/lint skills ( coding standards , plankton code quality ) with design level health scores and regression gates. Upstream: [codescene oss/codescene mcp server](https://github.com/codescene oss/codescene mcp server) Package: @codescene/codehealth mcp (stdio via npx) Security and boundaries Opt in (ECC): The codescene block in mcp configs/mcp servers.json is a template only. ECC plugin installs do not auto enable bundled MCP servers. Copy the entry into your config only if you want it. You can exclude it during ECC install/sync with ECC DISABLED MCPS=codescene,... . Credentials: No bundled token. Set CS ACCESS TOKEN yourself (see [getting a personal access token.md](https://github.com/codescene oss/codescene mcp server/blob/main/docs/getting a personal access token.md) in the upstream repo). Never commit tokens to the repo. What the tools read: When invoked, tools analyze files and git state in the local repository you point them at (paths you pass, plus branch context for analyze change set ). They do not run by themselves. For standalone mode, follow upstream privacy docs: [codescene mcp server README](https://github.com/codescene oss/codescene mcp server frequently asked questions) and [CodeScene policies](https://codescene.com/policies). Do not use this skill for secrets, credentials, or paths you do not want analyzed. If the MCP is unavailable (offline, bad token, server crash): Do not invent Code Health scores. Tell the user the check was skipped. Continue only with explicit user approval. Prefer lint/tests/verification loop for gating when MCP is down. Re enable checks once the server connects. When to Use User asks to review code quality , refactor a file, or check if AI changes degraded maintainability Before editing a hotspot , legacy module, or unfamiliar file Before commit or pull request when you need a maintainability safeguard After a large agent written diff — verify Code Health did not regress Pair with verification loop , tdd workflow , or /quality gate as a structural check (not a replacement for tests/lint) When to Activate Same triggers as When to Use above — this heading is what ECC uses for skill auto activation. How It Works 1. Connect the MCP server Copy the codescene entry from mcp configs/mcp servers.json into your harness MCP config. Claude Code ( ~/.claude.json → mcpServers ): Project scoped: merge the same block into .mcp.json at the repo root. Token setup is documented in the upstream repo (link above). Standalone mode does not require a paid CodeScene platform account for the four tools listed below. Restart the session and confirm the codescene server is connected before relying on scores. 2. Call standalone tools only Tool When to use code health review Full structural analysis before modifying a file code health score Quick numeric score after each change (delta check) pre commit code health safeguard Block commits that introduce Code Health regressions analyze change set Branch level check before opening a PR Do not call platform only tools (e.g. repository wide technical debt hotspot lists). Do not reference delta analysis — not available on standalone. 3. Interpret scores (1–10) Range Meaning Agent behavior 9.0–10.0 Green — healthy Safer to extend; still prefer vertical slices 4.0–8.9 Yellow — debt Tread carefully; no drive by refactors 1.0–3.9 Red — severe debt Narrow scope only 4. Run the feedback loop Before touching a file 1. Run code health review on the target path. 2. Record baseline score and listed code smells. 3. Plan the smallest change that addresses the task. Scope by score: below 5 — minimal diff only; 5–7 — no broad refactors; above 7 — safer to refactor, still verify after each edit. After each change 1. Run code health score on the same file. 2. Compare to the baseline from code health review . 3. If the score regressed , fix before continuing. Never mark the task done while the score is lower than when you started. Before every commit — run pre commit code health safeguard on the repository path. Before a PR — run analyze change set against the base branch (e.g. main ). Examples Example: Flask maintainability improvement On pallets/flask , an agent loop using only standalone tools: 1. code health review on a target module (baseline 4.82 ) 2. Targeted refactor addressing listed smells 3. code health score after each edit 4. pre commit code health safeguard before commit 5. analyze change set before PR Result: Code Health 4.82 → 9.1 (free standalone token only). Example: AGENTS.md enforcement block Paste into the project AGENTS.md or CLAUDE.md : Example: anti patterns vs correct loop Pairing with ECC ECC skill / flow Code Health MCP role coding standards Style/naming; Code Health = structure/complexity plankton code quality Write time lint/format; Code Health = pre/post edit structural gate verification loop / /quality gate Add structural regression check before "done" security review Security vs maintainability — use both when relevant tdd workflow Tests pass ≠ healthy design — check score after refactors Context tip: ECC recommends keeping MCP count low. Enable codescene when doing substantive edits; disable when not needed. Related Skills coding standards — baseline conventions plankton code quality — write time lint/format hooks verification loop — build/test/lint gate tdd workflow — test first development security review — security checklist documentation lookup — library docs via Context7 (orthogonal)

codehealth-mcp