About Over-Reach Detector
Over-Reach Detector is an MCP server and CLI for checking whether AI coding agents changed files beyond a task’s declared scope. It reports mismatched files and categories for review, but does not block or revert changes.
Key features
- 01Compare declared scope with modified files
- 02Match file paths with fnmatch globs
- 03Check declared and inferred categories
- 04Return JSON or Markdown reports
Use cases
- 01Audit an agent’s changes against task boundaries
- 02Identify out-of-scope files for human review
MCP support
It exposes the `check_scope_tool` to MCP-compatible coding agents over stdio. The tool reports scope mismatches; enforcement and rollback remain the caller’s responsibility.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEOver-Reach Detector (MCP server)
Detect and report when AI coding agents change files outside their declared task scope. Designed to plug into Cursor, Claude Code, and other MCP-compatible AI coding agents via the standard stdio transport.
Compares two things:
- Declared scope — the files (as fnmatch globs) and categories (tests, docs, infra, config, code) the task is allowed to touch.
- Actual diff — the files the AI actually modified.
If the actual diff exceeds the declared scope, the tool returns status=over_reach and lists the offending files and categories.
What this tool does and does not do
over-reach-detector is an audit/disclosure tool, not a sandbox and not a blocker.
It compares a declared task scope with the actual files/categories changed by an AI coding agent. If the actual changes exceed the declared scope, it reports that mismatch.
It does not prevent file writes by itself. It does not decide whether to revert, approve, or block a change. The caller remains responsible for enforcement, rollback, or human review.
Install
From PyPI:
pip install over-reach-detector
From source:
git clone https://github.com/choreoatlas/over_reach_detector
cd over_reach_detector
pip install -e .
Quick start
Run all tests: python -m pytest -v
Try the CLI directly: python -m over_reach_detector.detector --input fixtures/example_pr_1.json --format markdown
Use as MCP server
Start the server (stdio transport): over-reach-detector (or python -m over_reach_detector.server from source)
Register with your AI agent:
- Cursor: in
~/.cursor/mcp.json, add (afterpip install over-reach-detector):
{
"mcpServers": {
"over-reach-detector": {
"command": "over-reach-detector"
}
}
}
Dev / from source: use "command": "python", "args": ["-m", "over_reach_detector.server"] (run from repo root).
- Claude Code: after
pip install over-reach-detector, runclaude mcp add over-reach-detector over-reach-detector(writes to~/.claude.json). Dev / from source:claude mcp add over-reach-detector /absolute/path/to/python -m over_reach_detector.server.
The tool
check_scope_tool takes:
declared_files: list of fnmatch globs (e.g.["docs/*.md", "tests/*.py"])declared_categories: subset of["tests", "docs", "infra", "config", "code"]actual_files: list of file paths the AI modifiedoutput_format:"json"(default) or"markdown"
Returns a report with:
status:in_scope(within declared scope) |over_reach(reported mismatch) |emptyfile_overreach: files not matching any declared globcategory_overreach: inferred categories outside the declared set
Scope discipline
Current scope: CLI + MCP stdio server + 1 tool. Python only. fnmatch-based globs.
Out of scope (forbidden): code quality review, security audit, completeness governance, languages other than Python, multi-tool MCP servers, HTTP/SSE transport, GitHub Actions integration. These are deliberately deferred to later versions or never.
Example usage
Call check_scope_tool directly from Python (same logic the MCP server exposes):
import json
from over_reach_detector import server
result = server.check_scope_tool(
declared_files=["docs/*.md"],
declared_categories=["docs"],
actual_files=["docs/a.md", "scripts/extra.py"],
output_format="json",
)
report = json.loads(result)
print(report["status"]) # "over_reach"
print(report["file_overreach"]) # ["scripts/extra.py"]
License
MIT — see LICENSE.
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.