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  1. Home
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  3. Diffgate
Diffgate logo
Health: ActiveRecent health check succeeded.Last checked 9/17/2026, 4:16:13 PM

Diffgate

User RatingsBe the first to rate and review this MCP server!
View Repository5 GitHub StarsTotal stargazers on GitHub for the source repository (5 stars).Visit Website
code-reviewsecuritydeveloper-toolsmcp

Deterministic MCP guardrail that reviews changed lines for security and structural risks without using LLM tokens.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Automated check passedβ€” started and listed 7 tools correctly (1mo ago).
Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for srbsa/diffgate, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Tool Schemas (7) Directory Badge Claim listing AlternativesπŸ’° More in Finance & Fintech

Overview

DiffGate analyzes code changes over MCP and returns structured, deterministic findings scoped to changed lines. It checks security patterns and structural issues across eight languages, with tiered results for merge, review, or verification. Use it in an agent loop, editor, pre-commit hook, or CI to identify risky changes before they reach the repository.

Use cases

β€’Review agent-generated diffs before writing them to disk
β€’Flag security risks in changed code
β€’Run tests selectively for high-impact changes
β€’Check diffs for reinvented helpers and over-abstraction
β€’Gate changes using deterministic findings

Key features

β€’MCP server for coding-agent workflows
β€’AST-precise checks across eight languages
β€’Diff-scoped findings limited to changed lines
β€’Rules for SQL injection, SSRF, XXE, permissive CORS, prototype pollution, and hardcoded secrets
β€’Structural checks for reinvented helpers and over-abstraction
β€’Optional selective test execution for high-impact changes

Capabilities & Tool Schemas (7) ~1.2k tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Verified live Verified liveCaptured by calling this server’s live tools/list endpoint.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Diffgate.

diffgate_analyze

Analyze a file for code review findings. Only flags risk on lines changed vs the git baseline (diff-aware). Pass `content` to analyze unsaved or generated code before it is written to disk. When a code graph is available, public-surface findings carry an `impact` field (caller count, suggested reviewers, test gaps) and may be tier-adjusted β€” fix high-blast-radius findings before surfacing the code.

diffgate_check_staged

Check all staged (or working-tree) changes in a git repo for DiffGate findings. Returns overall tier, counts, and per-file findings across the whole diff, plus a `verdict` block (the agent autonomy ladder: pass/review/blocked overall, with a rung β€” block/escalate/autofix/advisory β€” per finding) so you can decide whether to surface the diff without reimplementing the rules.

diffgate_deep_review

Run an agentic deep review on a single high-impact (orange) finding. The model uses real repo tools (grep, read_file, find_references, git_blame) to investigate blast radius before rendering a verdict.

diffgate_explain

Get a concise AI explanation for a DiffGate finding. Faster than diffgate_deep_review β€” a single LLM call with no tool loops.

diffgate_capabilities

Report which DiffGate layers are active (core / code graph / LLM), which tools you can call right now without an error, and the agent autonomy budget (fix limit, escalation, trust source). Call this once up front so you know what's available instead of discovering it via thrown errors.

diffgate_guidelines

Review the diff against the repo's own coding guideline files (AGENTS.md, CLAUDE.md, .cursorrules, etc.), scoped per directory (nearest file wins). IMPORTANT: if the result has mode='host', NO external model was used β€” this is a SELF-REVIEW, not an independent gate: YOU (the calling agent) evaluate each group's `hunks` against its `guidelines` text using your own model. Treat host-mode results as ADVISORY only β€” never block the change on them. If mode='model', findings were produced by the configured provider and are returned directly.

Documentation Overview

DiffGate

npm version npm downloads VS Code Marketplace Open VSX License GitHub stars

A deterministic guardrail your coding agent runs on itself β€” before the code reaches your disk.

Every other check fires too late. A review bot needs a PR. A pre-commit hook runs after the agent has finished and moved on. DiffGate is an agent hook: your agent calls it over MCP, gets back structured findings in milliseconds for zero LLM tokens, and fixes the problem while it still has the context β€” then the same engine, same verdict runs in your editor, your pre-commit hook, and CI. Not a model grading its own homework: the same input always produces the same answer.

It stays quiet by construction. It grades only the lines that changed (🟒 merge Β· 🟑 glance Β· 🟠 verify), runs your tests only when a change earns it, and blocks only when it's earned β€” 0 false blocks on a public, versioned corpus you can rerun yourself with diffgate bench (BENCHMARK.md). Everyone claims low noise; this one ships the corpus.

Why this catches things a scanner doesn't β€” we measured what agents actually get wrong

Across local and frontier models: 0% classic OWASP bugs (SQL injection, XSS, hardcoded secrets) in code written from scratch. But the same frontier model that wrote flawless greenfield code reintroduced security footguns in 13% of edits β€” an unguarded recursive merge (prototype pollution), a bare cors() (any origin), a path built from request data with no containment check. Editing existing code is most of what an agent does, and the residue lives in the diff, not in the textbook. Rerun it yourself with diffgate marginal. The measurement β†’

The same shift shows up in third-party maintainability data: GitClear's 2026 analysis finds block duplication up 81% since 2023 (40.3 β†’ 73.0 per million changed lines) and cross-file function connectivity down 35% β€” agents reinvent code rather than reuse it. That's what the reinvented-helper rule is for (docs/STRUCTURAL-RULES.md).

DiffGate demo: diffgate check on a real repo, mostly green with one orange finding and its reason

TierMeaningWhat you doExamples
🟒 GreenSafe / self-containedmerge freelycomments, local logging
🟑 YellowReview (soft dependency)take a lookdeprecated APIs, raw SQL, network calls, dependency edits
🟠 OrangeHigh-impact, gate itverify before mergeschema/migrations, hardcoded secrets, auth/crypto, public-API changes, injection sinks

Why an agent hook, not another review bot

Every check in the pipeline fires after the code exists, and each one is later than the last:

Fires whenProblem
Review bot (CodeRabbit, Greptile)a PR existsthe code is finished, defended, and someone is waiting on it
Pre-commit hookyou're done and committingthe agent has moved on; you're re-loading context to fix it
DiffGate over MCPthe agent is still writingit fixes its own output before the code lands

Being early is not the only thing that matters, though β€” it has to be quiet, or the agent learns to ignore it. Review bots comment after the PR exists; linters and scanners flag everything they see. Neither guarantees the risky line gets discussed: we scanned 350 merged AI-assisted PRs β€” of the 109 with flagged AI-attributed changes, only 3 drew public discussion from any human besides the author. DiffGate decides what deserves your attention, your tests, or a block, and stays quiet otherwise. That's the whole product:

  • Diff-scoped. Findings report only on the lines that changed, against the committed baseline β€” no whole-file noise, no re-litigating code you didn't touch.
  • Tiered triage, not a flat list. Three tiers route attention: green merges, yellow is a glance, orange is gated.
  • The gate runs your tests β€” selectively. On an orange change, DiffGate runs your testCommand and shows the real exit code and output. Green and yellow pass instantly. The pre-commit hook is fast because tests fire only when a change is genuinely high-impact.
  • Earns the right to block. Broad cross-language injection findings stay advisory on their own; they escalate to a blocking finding only when the optional code graph proves the sink is reachable from an untrusted entry point (an HTTP/event handler). Recall from the rules, the right to block from the graph.
  • Change-impact aware. With an optional code graph, a finding carries its cross-file blast radius β€” caller counts, suggested reviewers, untested call sites β€” and an exported symbol nobody calls is de-escalated. Cross-file context makes reviews quieter, not louder.
  • Fast. A review runs in milliseconds on the changed lines β€” quick enough to sit in the agent and editor inner loop, not only in CI.
  • Provably low-noise. diffgate bench runs a versioned corpus offline: 100% precision / 0 false blocks on clean changes. Reproduce it yourself β€” that's the point of shipping the corpus. See BENCHMARK.md.

The measurement is reproducible

The 0% / 13% numbers aren't a marketing line β€” they come from a scripted experiment (four models from local to frontier, greenfield vs. edit mode, Wilson confidence intervals) that you can rerun with diffgate marginal. Methodology, per-model tables, and caveats: docs/MEASUREMENT.md. DiffGate's security rules are tuned to that measured residue, not to maximizing rule count.


Quick start

Terminal
npm install -g diffgate-review
cd your-repo
diffgate init                    # auto-detects language + test command, writes .diffgate.json
diffgate check --since=HEAD~20   # see what it catches in your own history β€” no PR required
diffgate check                   # review your pending changes right now

No git history or uncommitted changes yet? See the output on bundled examples first:

bash
diffgate init --demo   # live scan, no config or git changes needed

The surfaces (one shared engine, one verdict)

1. In your coding agent (via MCP)

The highest-leverage spot: the agent self-checks generated code before it's written to disk, gets back structured findings (zero LLM tokens), and surfaces what it corrected (original + fix + why) instead of silently rewriting. A trustworthy, deterministic self-check is what makes it safe to grant the agent more autonomy.

bash
# Claude Code β€” one command:
claude mcp add diffgate -- diffgate mcp

# One-click via Smithery (zero config):
npx @smithery/cli install diffgate-review --client claude

# Cursor β€” add to MCP settings:
# { "diffgate": { "command": "diffgate", "args": ["mcp"] } }

Or one-click in Claude Desktop: download diffgate.mcpb and open it. The server also exposes prompts and resources; see MCP.md.

2. In your editor (VS Code / Cursor)

Inline squiggles on changed lines, hover cards (why Β· who owns it Β· quick-fix), a Risk Review tree, a status-bar summary, and Deep Review (agentic blast-radius analysis for orange findings). The same verdict you'd get from the CLI, on the diff you're reviewing.

Install from the VS Code Marketplace or Open VSX (Cursor / Windsurf / Gitpod).

3. On the command line β€” and in CI

diffgate check reviews your diff and exits non-zero on high-impact findings: a pre-commit hook locally, the same gate in your pipeline.

bash
diffgate install-hook  # adds .git/hooks/pre-commit; only runs tests on 🟠 orange changes

The local loop is the wedge β€” fix while the context is fresh β€” and the same engine runs as a PR gate so the verdict carries to where it's enforced for the whole team. See docs/TEAM.md for the GitHub Action, shared learnings, and org policy packs. CI runs can optionally layer an external scanner (Semgrep) through the same gate for broader language coverage β€” advisory-only, off by default (docs/CONFIG.md).

Common commands:

bash
diffgate check                 # review pending changes (the gate)
diffgate check --staged        # staged-only (pre-commit)
diffgate check --since=HEAD~20 # audit recent history, per-commit (see below)
diffgate check --agent         # machine verdict for coding agents
diffgate scan <path>           # analyze files directly (no git needed)
diffgate watch                 # live review as you edit
diffgate guidelines            # review diff against AGENTS.md / CLAUDE.md etc.
diffgate feedback <rule> <f> <l> --dismiss   # suppress a false positive (shared via git)
diffgate mcp                   # start the MCP stdio server

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
5
Stargazers on the source repository.
npm downloads
27k
Package downloads in the last 30 days.
Last commit
1mo ago
Most recent push to the default branch.
Availability
100%
Our rolling endpoint + install checks that succeeded.
Install check
Passed
Our sandbox started it and listed its tools.
Tools exposed
7
Callable tools this server registers over MCP.
Directory activity
1 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Diffgate

No. Its checks are deterministic and return findings without using LLM tokens.

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Technical Specs & Signals

CategoryπŸ’°Finance & Fintech
PricingFree
More technical detailsExpand β–Ύ
AuthNo auth required
Last updatedAug 9, 2026
10/11 checks healthy over the last 42d
Views1
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars5
GitHub Star CountTotal stargazers on GitHub representing community popularity (5 stars).
Last commit1mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Jul 27, 2026
npm downloads27,958/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
73Quality signal: Great Β· 73/100How this signal is calculated β–Ύ
Server availability25/25
Verified ownership10/20
Documentation & tools30/30
Adoption & activity8/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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Scanned 8/17/2026 via OSV.dev

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