codehealth-mcp

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.

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---
name: codehealth-mcp
description: 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.
origin: community
---

# 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`):

```json
"codescene": {
  "command": "npx",
  "args": ["-y", "@codescene/codehealth-mcp"],
  "env": {
    "CS_ACCESS_TOKEN": "YOUR_CS_ACCESS_TOKEN_HERE"
  }
}
```

**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`:

```md
## Code Health (CodeScene MCP)

Before modifying any file: run `code_health_review`, note score and issues.

- Score below 5: problematic range — scope changes narrowly.
- Score 5–7: warning range — no broad refactors.

After each change: run `code_health_score` to verify delta.

- If score regressed: fix before continuing; never declare done if score dropped.

Before every commit: run `pre_commit_code_health_safeguard`.

Before PR: run `analyze_change_set`.
```

### Example: anti-patterns vs correct loop

```markdown
# BAD: Edit first, check later
[large refactor without code_health_review]

# BAD: Ignore score drop
"Tests pass" → mark task done while Code Health decreased

# BAD: Broad refactor on red-score file (below 5)
Drive-by cleanup across the module

# GOOD: review → small change → score → commit safeguard → analyze_change_set
```

## 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)

Source

Creator's repository · affaan-m/everything-claude-code

View on GitHub

Security

Security checks in progress
Results will appear here once audits complete
What this skill can do
Reads your filesConnects to the internetRuns code on your machine
Checked by 3 independent security firms
Does it try to trick the AI?Not yet checkedPending · Gen Agent Trust Hub
Does it sneak in hidden code?Not yet checkedPending · Socket
Does it have known bugs?Not yet checkedPending · Snyk