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 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 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 and CodeScene 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):

"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

Run code_health_review on the target path.
Record baseline score and listed code smells.
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

Run code_health_score on the same file.
Compare to the baseline from code_health_review.
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:

code_health_review on a target module (baseline 4.82)
Targeted refactor addressing listed smells
code_health_score after each edit
pre_commit_code_health_safeguard before commit
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:

## 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

# 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)

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 Package: @codescene/codehealth-mcp (stdio via npx)

Security and boundaries

Credentials: No bundled token. Set CS_ACCESS_TOKEN yourself (see getting-a-personal-access-token.md in the upstream repo). Never commit tokens to the repo.

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):

"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.

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

Run code_health_review on the target path.
Record baseline score and listed code smells.
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

Run code_health_score on the same file.
Compare to the baseline from code_health_review.
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:

code_health_review on a target module (baseline 4.82)
Targeted refactor addressing listed smells
code_health_score after each edit
pre_commit_code_health_safeguard before commit
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:

## 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

# 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)

codehealth-mcp

让 AI 帮你安装

Code Health MCP (CodeScene)

Security and boundaries

When to Use

When to Activate

How It Works

1. Connect the MCP server

2. Call standalone tools only

3. Interpret scores (1–10)

4. Run the feedback loop

Examples

Example: Flask maintainability improvement

Example: AGENTS.md enforcement block

Example: anti-patterns vs correct loop

Pairing with ECC

Related Skills

链接

创作者

Code Health MCP (CodeScene)

Security and boundaries

When to Use

When to Activate

How It Works

1. Connect the MCP server

2. Call standalone tools only

3. Interpret scores (1–10)

4. Run the feedback loop

Examples

Example: Flask maintainability improvement

Example: AGENTS.md enforcement block

Example: anti-patterns vs correct loop

Pairing with ECC

Related Skills