repro-mcp 0.1.0

Reproducibility logging and rule enforcement MCP server for scientific computing

repro-mcp

An MCP server that brings reproducibility logging to AI-assisted scientific computing.

Git records what changed. repro-mcp records why — logging prompts, responses, methodological decisions, and environment snapshots to human-readable markdown files that live alongside your code.

Built for researchers working under privacy or funding constraints who run models locally and need an audit trail that holds up to peer review.

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Why this exists

AI coding assistants are increasingly used in scientific workflows, but the interaction between a researcher and an AI — the prompts, the reasoning, the alternatives considered — disappears when the session ends. This matters because:

• A methods section can't cite a conversation
• A future collaborator can't reproduce your reasoning, only your code
• Environment drift silently breaks analyses months later

repro-mcp captures all of this locally. No cloud dependency. No data leaves the machine.

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How it works

repro-mcp is a Model Context Protocol server. Any MCP-compatible AI client (Claude Code, Cline, Cursor, Continue) can connect to it. It exposes tools the AI can call to:

• Open a session and snapshot the environment
• Log each prompt/response exchange
• Record significant decisions and the alternatives that were rejected
• Check code against reproducibility rules before it runs
• Close the session with a git diff summary

All output goes to .repro/ in your project directory — plain markdown, git-friendly, human-readable.

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Installation

Requires Python 3.11+. The recommended way is via uv:

# No install step — uvx runs it in an isolated environment
uvx repro-mcp

Or with pip:

pip install repro-mcp

Or from source:

git clone https://github.com/ABindoff/repro-mcp.git
cd repro-mcp
pip install -e .

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Configuration

Claude Code

Add it manually to .mcp.json in your project root (recommended — checked into git alongside your code):

{
  "mcpServers": {
    "repro-mcp": {
      "command": "uvx",
      "args": ["repro-mcp"]
    }
  }
}

Or register globally via the Claude Code CLI:

claude mcp add repro-mcp --scope user -- uvx repro-mcp

Verify it's running with claude mcp list.

Auto-start via hooks (recommended)

Calling session_start manually triggers a permission prompt. The better approach is a UserPromptSubmit hook that auto-starts a session on your first message — no prompts, no manual steps.

Add this to ~/.claude/settings.json (global, applies to all projects):

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "repro start",
            "timeout": 30
          }
        ]
      }
    ]
  }
}

The repro command is installed alongside repro-mcp. It auto-detects the project name from your current directory and uses "Claude Code session" as the default goal. You can override either:

repro start my-project "Fit Cox model to patient cohort"

The hook is idempotent — if a session is already active it exits silently, so firing on every message is safe.

Ending a session

Close the active session from the terminal when you're done:

repro end success       # or: abandoned, inconclusive
repro end success --notes "Switched to Efron method"

Or call session_end via the MCP tool if you've allowlisted repro-mcp tools (add "mcp__repro-mcp__*" to the permissions.allow array in ~/.claude/settings.local.json).

Allowlisting MCP tools (optional)

If you want to call repro-mcp tools directly (e.g. log_decision, log_exchange) without permission prompts, add this to ~/.claude/settings.local.json:

{
  "permissions": {
    "allow": ["mcp__repro-mcp__*"]
  }
}

Cline (VS Code extension)

Open Cline settings → MCP Servers → add:

{
  "repro-mcp": {
    "command": "python",
    "args": ["-m", "repro_mcp.server"],
    "cwd": "${workspaceFolder}"
  }
}

Cline SDK / CLI

Use the afterModel hook to log every turn automatically:

from cline import AgentPlugin

repro_plugin: AgentPlugin = {
    "hooks": {
        "afterModel": async ({ snapshot, assistantMessage }) => {
            await mcpClient.callTool("log_exchange", {
                "session_id": your_session_id,
                "prompt": snapshot.lastUserMessage,
                "response": assistantMessage.content,
            })
        }
    }
}

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Tools

Tool	Description
session_start	Open a session, snapshot the environment, create the log file
session_end	Close the session, write git diff summary, update the index
log_exchange	Log a prompt/response pair with optional tags
log_decision	Log a methodological decision with rationale and alternatives
snapshot_environment	Capture a mid-session environment snapshot
check_rules	Check code against reproducibility rules before running

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Log format

Each session produces .repro/logs/YYYY-MM-DDTHHMMSS.md:

# Session: 2026-05-14T143022
**Project:** survival-analysis
**Goal:** Fit Cox model to patient cohort, compare AIC across covariates
**Branch:** feature/cox-model
**Git hash:** a3f9c12d8e41

### Environment snapshot `2026-05-14T14:30:22+00:00`
- **Python:** 3.11.4
- **Platform:** Linux 6.5.0 (x86_64)
- **Git:** `a3f9c12d8e41` (feature/cox-model)

**Packages:**

lifelines==0.29.0 numpy==1.26.4 pandas==2.2.1

---

## Exchange — 14:30:45
**Prompt:** How should I handle tied survival times in the Cox model?
**Response:** The three standard methods are Breslow, Efron, and Exact...
**Tags:** `model-fit`

---

## Decision — 14:47:12
**Decision:** Use Efron method for tie handling
**Rationale:** More accurate than Breslow when tie rate exceeds ~5%; our cohort has ~12% tied events
**Alternatives considered:** Breslow (rejected — bias at this tie rate); Exact (rejected — O(n!) complexity at n=14k)

---

## Session close — 15:45:00
**Outcome:** success

**Git diff summary:**

3 files changed, 142 insertions(+), 7 deletions(-)

An index of all sessions is maintained at .repro/index.md.

---

Rules

repro-mcp ships with five built-in rules. Configure them by copying repro_defaults/rules.yaml to .repro/rules.yaml in your project.

Rule	Severity	What it checks
random-seed	error	Any RNG call must have a seed set in scope
env-pinned	warn	requirements.txt / environment.yml must pin versions
no-hardcoded-paths	error	No absolute paths outside of config files
no-inplace-data-mutation	warn	Raw data directories should not be written to
gpu-nondeterminism	info	CUDA ops detected without determinism flags

To disable a rule:

# .repro/rules.yaml
rules:
  gpu-nondeterminism:
    enabled: false

---

Project layout

your-project/
└── .repro/
    ├── index.md              # table of all sessions
    ├── rules.yaml            # rule configuration (optional)
    └── logs/
        ├── 2026-05-14T143022.md
        └── 2026-05-14T160011.md

Add .repro/logs/ to .gitignore if you want to keep logs local, or commit them to give collaborators the full audit trail.

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Roadmap

• Publish to PyPI
• Cline upstream PR: PostAssistantTurn hook for automatic log_exchange calls in the VS Code extension
• data-provenance rule: flag data loading that doesn't reference a hash or versioned source
• Export session log as a structured methods-section draft

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Contributing

Issues and PRs welcome. The Cline integration gap (automatic per-turn logging in the VS Code extension) is the most impactful open problem — see the Cline repo if you want to help.