codehealth-mcp

A code-quality review tool that uses CodeScene to examine a local repository's files and Git changes. It focuses on how easy code is to maintain, beyond basic style checks.

In plain words
What is it for?
It is for reviewing code health, checking the effect of edits, and setting quality checks for commits and pull requests.
Why use it?
It helps detect structural problems and quality regressions before or after code changes, including changes made by an AI agent.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/junmystery/agent-guidance-python/codehealth-mcp
Any agent
npx skills add JunMystery/Agent-Guidance-Python --skill codehealth-mcp
Clone the repo
git clone --depth 1 https://github.com/JunMystery/Agent-Guidance-Python

Made for: Claude Code, Codex.

Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,800 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 98% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00055 $0.01800
Opus 5 $0.00028 $0.00900
Sonnet 5 $0.00011 $0.00360
Haiku 4.5 $0.00006 $0.00180

Measured 3d ago against content hash 24dd54b5758a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codehealth-mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

98% identical to codehealth-mcp — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/codehealth-mcp/SKILL.md · 167 lines

How it starts

The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

Read the full file on GitHub · 167 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 167 lines · 55 tokens per session scan A 24dd54b5758a

Subscribe to this mod's changes

codehealth-mcp is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 1,800 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to codehealth-mcp, differing in 3 lines, and is treated as a copy.

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