contrarian 0.2.0

MCP server adding an independent second LLM perspective (Judge B) with full project graph context

Contrarian

MCP server that adds a second LLM perspective (Judge B) directly inside Claude Code.

Claude Code is Judge A — it already has full project context. Contrarian adds Judge B, an independent model from a different lab, via a single tool call. Same rubric, different priors, no context switch.

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How it works

1. Claude Code invokes contrarian_review() — no args for auto-detect, or pass specific files.
2. Contrarian resolves the input: git diff, last commit, or full audit (in that order).
3. Judge B (external model) reviews against JUDGE.md in the project root.
4. Findings are returned inline and appended to REPORT.md for the dialogue log.

Auto-detect order:

• diff — any staged/unstaged changes or untracked files
• last-commit — clean tree → reviews the last commit
• audit — forced with audit=true, or nothing else found

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Installation

Requires Python 3.10+. You'll need a free API key from DeepSeek (no credit card required).

pip install contrarian
contrarian setup

contrarian setup prompts for your API key, then writes the MCP server config to ~/.claude/.claude.json automatically. Restart Claude Code after running it.

That's it.

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Manual config (alternative)

If you prefer to configure manually, add to ~/.claude/.claude.json:

{
  "mcpServers": {
    "contrarian": {
      "type": "stdio",
      "command": "contrarian",
      "args": [],
      "env": {
        "JUDGE_B_API_KEY": "sk-or-...",
        "JUDGE_B_BASE_URL": "https://openrouter.ai/api/v1"
      }
    }
  }
}

Default provider is DeepSeek (api.deepseek.com, model deepseek-chat). Change JUDGE_B_BASE_URL and JUDGE_B_MODEL to use any OpenAI-compatible provider (Gemini, Groq, Ollama, OpenRouter, etc.).

Anthropic models are blocked at runtime — Judge B must come from a different lab than Judge A.

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Usage

Inside any Claude Code session, the tool is available as contrarian_review.

contrarian_review()                                    # auto-detect mode
contrarian_review({ path: "src/auth.py" })             # single file, diff
contrarian_review({ path: "src/auth.py", full: true }) # full file
contrarian_review({ paths: ["src/a.py", "src/b.py"] }) # multi-file, reviewed as a unit
contrarian_review({ audit: true })                     # force full repo walk
contrarian_review({ model: "google/gemini-3.1-pro-preview" }) # one-off model override

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Rubric

Place a JUDGE.md at the project root to customize what Judge B looks for. Without it, a built-in fallback rubric applies.

The built-in rubric covers four dimensions:

Dimension	What to find
Exactitude	Logic errors, wrong assumptions, incorrect implementation
Missing	Unhandled cases, absent validation, unconsidered implications
Premises	Assumptions that could be wrong
Alternatives	Fundamentally different approaches

Output is always JSON:

{
  "exactitude": { "verdict": "ok|warn|fail", "findings": [] },
  "missing": [],
  "premises": [],
  "alternatives": []
}

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Report log

Findings are appended to REPORT.md at the project root under [Judge B] blocks. Claude Code annotates findings under [Judge A] blocks. Judge B reads previous exchanges before each review — closed findings (addressed, won't fix, disagree) are not re-raised.

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Phase roadmap

• Phase 2 (current): MCP server + project graph context. Auto-detect mode. Python rewrite.
• Phase 3: Operational hardening — token budget guard, graph latency, detached HEAD handling.
• Phase 4: Agent watchdog — autonomous background review on commit.