Periscope

periscope-mcp

An MCP server that gives AI agents 74 Playwright tools to QA, test, and analyze web apps — static sites, SPAs, and apps behind a login — returning hard verdicts, not screenshots to squint at. Not a thin wrapper around browser APIs; the tools are shaped around how agents actually work:

• Hard results, not screenshot-squinting — assert_condition returns passed: true/false with the actual value; checks return structured issues.
• One call instead of ten — auto_fill_form detects, infers, and fills a whole form; interact_and_test batches 25 action types with checks; test_project crawls and audits an entire site.
• Real web-app testing — persistent authenticated sessions (form/basic/ cookie auth, plus a visible interactive login for 2FA/SSO/CAPTCHA that then runs headless), multi-step flows, network mocking, state snapshots, and real INP measured from the interactions it drives.
• Honest responses — failures say what happened and what to do next (expired session vs. browser crash vs. eviction); silent no-ops like ignored drags come back flagged, not as fake success.
• Debugging built in — captured API response bodies, console/network logs, network mocking, and state snapshots/diffs, no setup calls needed.
• Audits agents can't get from a browser binding — accessibility, SEO, and GEO/agentic-search readiness (robots.txt AI-crawler access, llms.txt, WebMCP), plus real Lighthouse.

Playwright + headless Chrome underneath; site crawling, responsive testing, and screenshot diffing on top. Works with any MCP client — Claude Code, Codex, Cursor, Windsurf, Gemini CLI, custom agents, or anything else that speaks MCP over stdio.

Why not just playwright-mcp?

playwright-mcp is excellent at what it is: general browser control over MCP, with tools that mirror Playwright's own API. If the job is "browse this site, click around, extract something," use it.

Periscope exists for a different job: testing and auditing a site or web app, then reporting findings — and its tools encode the testing knowledge an agent would otherwise have to reinvent every session:

The two aren't rivals — an agent can happily use playwright-mcp for browsing tasks and Periscope when it's wearing the QA hat. Periscope's design bets are simply about that hat: fewer, higher-level calls; structured verdicts instead of raw page state; and errors written to tell the agent what to do next.

Architecture

MCP client (AI agent)  -->  MCP Server (stdio)  -->  Playwright (Headless Chrome)
                                 |                         |
                                 +-- Projects (JSON)       +-- Persistent Sessions
                                 +-- Screenshots (PNG)     +-- Network Interception
                                 +-- Reports (JSON)        +-- Device Emulation
                                 +-- Videos (WebM)

How it works: your MCP client connects to this server over stdio. The server exposes 74 tools the agent can call to create projects, configure authentication, crawl websites, run static checks, and interactively test web applications using persistent browser sessions. Results (JSON + screenshots + videos) are returned to the agent for analysis.

Project Structure

periscope-mcp/
├── server.py              # MCP server entry point (stdio wiring + dispatch)
├── tool_schemas.py        # All 74 MCP tool definitions (schemas)
├── runtime.py             # Shared singletons (project store, sessions, browser)
├── coercion.py            # Argument coercion for MCP clients with stale schemas
├── handlers/              # Tool handlers, grouped by category
│   ├── registry.py        # @tool(name) decorator + HANDLERS registry
│   ├── projects.py        # create/list/get/delete project
│   ├── auth.py            # form login, basic auth, cookies, copy_auth
│   ├── static_testing.py  # test_url, crawl, test_project, reports, responsive
│   ├── session_tools.py   # open/close/list sessions, viewport, history
│   ├── interactive.py     # click, fill, steps, element queries, dialogs
│   ├── analysis.py        # forms, links, keyboard nav, tables, toasts, contrast
│   ├── advanced.py        # network mocking, storage, iframes, emulation, recording
│   ├── agent_speed.py     # assertions, smart find, auto-fill, snapshots
│   ├── web.py             # web_search, web_fetch
│   ├── discovery.py       # describe_tools catalog
│   └── system.py          # periscope_system: status, self-update, agents_md
├── tester.py              # Playwright browser control + test orchestration
├── crawler.py             # Page discovery (BFS crawl, same-domain only)
├── projects.py            # Project CRUD + auth config storage
├── auth.py                # Authentication handlers (form, basic, cookies)
├── sessions.py            # SessionManager + PageSession — persistent page lifecycle
├── interactions.py        # Interaction primitives (click, fill, execute_steps)
├── utils.py               # Screenshot comparison (Pillow pixel diff)
├── config.py              # Global settings (timeouts, paths, session limits)
├── checks/
│   ├── visual.py          # Broken images, favicon, overflow, small text
│   ├── accessibility.py   # Alt text, labels, headings, lang, ARIA, keyboard nav
│   ├── functionality.py   # Broken links, forms, SEO, performance, link checker
│   └── geo.py             # GEO/agentic search: robots.txt AI crawlers, llms.txt, WebMCP, JSON-LD
├── tests/                 # Unit tests (no browser) + tests/e2e/ (real browser + fixture pages)
├── data/                  # Created at runtime (gitignored — contains credentials)
├── Dockerfile
├── docker-compose.yml
└── .mcp.json.example      # MCP registration template (copy to .mcp.json)

Prerequisites

• Python 3.11+
• Playwright + Chromium browser

Installation (Local)

Quick install (Debian/Ubuntu)

One command — clone and install:

git clone https://github.com/segentic-lab/periscope-mcp.git && cd periscope-mcp && ./install.sh

Fully unattended (no confirmation prompts):

git clone https://github.com/segentic-lab/periscope-mcp.git && cd periscope-mcp && ./install.sh -y

Already cloned? Just run ./install.sh from the repo directory.

The script installs apt prerequisites, creates the venv, installs Python dependencies and Playwright's Chromium, runs a headless self-test, and generates mcp-config.json with the correct absolute paths for this install (copy or merge it into your project's .mcp.json). Useful flags:

• ./install.sh --system-chromium — use an existing Chromium/Chrome (sets CHROMIUM_PATH) instead of downloading Playwright's build
• ./install.sh --skip-deps — never touch apt / use sudo
• ./install.sh -y — non-interactive (no confirmation prompts)

On any other platform the script doesn't modify your system — it prints the exact commands to run for your OS (./install.sh --manual macos|fedora|arch|suse|windows to pick explicitly).

Updating

./update.sh

Pulls the latest source from GitHub (git pull --ff-only) and refreshes the install: Python dependencies, Playwright browser (kept on system Chromium if that's what the install uses), the registry + headless-launch self-test, and a regenerated mcp-config.json. Works on any platform with an existing install. Your data/ directory (projects, credentials, screenshots, reports) is never touched.

• ./update.sh --force — stash local modifications to tracked files first (recover with git stash pop)
• ./update.sh --full — also re-check apt prerequisites on Debian/Ubuntu (uses sudo)

If you have local modifications, the script refuses and lists them instead of overwriting.

Manual install

# Clone the repo
cd periscope-mcp

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install Chromium for Playwright
playwright install chromium

Installation (Docker)

docker compose up -d

See Docker Deployment section below.

Connecting an MCP Client

Periscope is a standard stdio MCP server: point any MCP client at venv/bin/python server.py and you're done. ./install.sh generates mcp-config.json with the correct absolute paths for your machine; most clients accept that shape directly:

{
  "mcpServers": {
    "periscope": {
      "command": "/path/to/periscope-mcp/venv/bin/python",
      "args": ["/path/to/periscope-mcp/server.py"]
    }
  }
}

Client-specific examples:

• Claude Code — copy the config into the project as .mcp.json (cp .mcp.json.example .mcp.json and adjust paths), or run claude mcp add periscope -- /path/to/venv/bin/python /path/to/server.py
• Cursor / Windsurf — add the block above to ~/.cursor/mcp.json / ~/.codeium/windsurf/mcp_config.json
• Codex CLI — add to ~/.codex/config.toml: [mcp_servers.periscope] with command and args as above
• Custom agents — any MCP SDK client can spawn the server over stdio with the same command and args

After configuring, restart your client.

Teaching your agent to use the tools

Two options, depending on your agent:

• Claude Code (recommended): install the skill. SKILL.md (repo root; also exposed at skills/periscope/ in the Claude Code skill layout) is a Claude Code skill — it auto-triggers on web-testing tasks and loads a distilled operating guide (workflow decision table + the pitfalls) only when needed, costing ~0 context otherwise:

  ln -s "$(pwd)/skills/periscope" ~/.claude/skills/periscope
  

  A symlink keeps it current with ./update.sh (copy the folder instead if you prefer a frozen version).

• Any other MCP client: paste the guide. AGENTS.md contains a ready-made system-prompt block — workflows, tool-selection guidance, and known pitfalls. Paste its contents into your agent's system prompt (or custom instructions).

Either way, the agent can always fetch the current full guide from the running server via periscope_system(action="agents_md") and the complete catalog via describe_tools().

MCP Tools Reference (74 tools)

Project Management (4 tools)

Authentication (7 tools)

For logins that can't be automated — 2FA/MFA, SSO/OAuth redirects, CAPTCHA, magic links — use interactive_login (opens a real browser window; requires a display on the server), complete the login yourself, then save_login. It captures the authenticated session (cookies + localStorage) into the project, and every future headless session reuses it. Re-run when the session expires (Periscope flags that automatically — see the auth-expiry detection in test_project).

Static Testing (3 tools)

Results (4 tools)

Session Management (5 tools)

Sessions keep browser pages alive across tool calls, enabling multi-step interactive workflows.

set_viewport presets: mobile_sm (320x568), mobile (375x812), mobile_lg (428x926), tablet (768x1024), tablet_lg (1024x1366), laptop (1366x768), desktop (1920x1080), desktop_lg (2560x1440)

Interactive Actions (7 tools)

interact_and_test supports 25 step actions: click, force_click, fill, force_fill, type, select, select_option, wait, wait_for, wait_for_text, screenshot, navigate, hover, press_key, check, uncheck, scroll_to, scroll_within, evaluate_js, drag, right_click, go_back, go_forward, upload_file, wait_for_network

Analysis (10 tools)

Workflow Speed (8 tools)

Advanced Testing (9 tools)

Recording & Console (3 tools)

AI Agent Speed Tools (10 tools)

Web, Discovery & System (4 tools)

Test Checks

Visual (checks/visual.py)

• Broken images (incomplete load or 0 natural width)
• Missing favicon
• Horizontal overflow / layout issues
• Very small text (< 12px)
• Missing body background color
• Images without explicit width/height dimensions

Accessibility (checks/accessibility.py)

• Images missing alt text (decorative images exempt: alt="", role="presentation"/"none", aria-hidden)
• Links and buttons without accessible names (checks text, aria-label, resolvable aria-labelledby, title, img[alt], svg <title>; aria-hidden elements exempt)
• Form inputs without associated labels (label[for], wrapping label, aria-label/aria-labelledby, title)
• Heading hierarchy (missing H1, multiple H1, skipped levels)
• Missing lang attribute on <html>
• Duplicate id values (break label[for] and aria references)
• ARIA validity: unknown role values, aria-labelledby/describedby/controls/owns/activedescendant references to non-existent ids
• Missing skip navigation link (scans the first 5 links)
• Elements with tabindex > 0
• Keyboard navigation audit (tab order, visible focus indicators, element-identity cycle detection) — via test_keyboard_navigation tool

Functionality (checks/functionality.py)

• Broken internal links (HTTP HEAD check, up to 20 links in check_functionality)
• Comprehensive link checker with external link support (up to 100 links) — via check_links tool
• Forms without action or submit button
• Orphan buttons outside forms
• External links missing target="_blank"
• Required form field count
• Autocomplete disabled inputs

SEO (checks/functionality.py -> check_seo)

• Page title: missing, too long (> 60 chars), or very short (< 15 chars)
• Meta description: missing, too long (> 160 chars), or very short (< 50 chars)
• Missing viewport meta tag
• Missing canonical URL
• H1 heading: missing or more than one
• Open Graph: missing entirely, incomplete core tags (og:title/description/image/url), non-absolute og:image, missing twitter:card
• JSON-LD structured data: missing or unparseable blocks
• noindex via robots meta or X-Robots-Tag response header
• robots.txt blocking search engine crawlers (Googlebot, Bingbot, DuckDuckBot, ...) — error if all are blocked
• Site-wide (via test_project): duplicate titles / meta descriptions across pages, reported under site_issues

GEO / Agentic Search (checks/geo.py -> check_geo)

Generative Engine Optimization — is the site readable and usable by AI crawlers, answer engines, and in-browser agents:

• robots.txt blocking AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and 11 more)
• llms.txt presence and format compliance (Markdown with at least one H1)
• WebMCP integration: declarative <form toolname> annotations present and complete (tooldescription), form coverage ratio, and — when the browser exposes document.modelContext — registered tool enumeration with schema/name/description validation
• JSON-LD structured data presence (what answer engines cite from)

robots.txt and llms.txt are fetched once per origin and cached for the server's lifetime.

Performance (checks/functionality.py -> get_performance_metrics)

• DOM content loaded time (ms)
• Full page load time (ms)
• First paint / first contentful paint (ms)
• Core Web Vitals (lab values via buffered PerformanceObserver): Largest Contentful Paint (ms), Cumulative Layout Shift, Total Blocking Time approximation from long tasks (+ long-task count)
• Interaction to Next Paint (INP) — interaction_to_next_paint_ms: the real INP, measured from Event Timing entries for the interactions Periscope drives (null until you've interacted). This is a genuine field-style measurement, not the TBT lab proxy — Lighthouse can't produce INP in lab mode at all.
• Resource count
• Total transfer size (bytes / KB)

For scored, Lighthouse-official metrics use the run_lighthouse tool — it runs the real Lighthouse CLI (requires Node.js) and returns 0-100 category scores, official Core Web Vitals, and failed audits, saving the full JSON report to data/reports/.

INP time series (get_interaction_log)

Because Periscope drives real interactions, it can log each one's INP over an extended interactive test. get_interaction_log(session_id, format="json"|"csv") writes a file to data/reports/ — one row per interaction (t_ms, epoch_ms, inp_ms, type, target, url) plus percentile stats (p50/p75/p90/p98/worst) — for graphing INP over time. clear=true resets the recording. Records are capped per session (MAX_INTERACTION_LOG, oldest dropped).

Test Output Format

Each test_url call returns:

{
  "url": "https://example.com",
  "status": "success",
  "status_code": 200,
  "title": "Page Title",
  "screenshot_path": "/path/to/screenshot.png",
  "load_time_ms": 1500,
  "issues": [
    {
      "type": "accessibility",
      "severity": "error",
      "message": "3 images missing alt text",
      "details": ["img1.png", "img2.png", "img3.png"]
    }
  ],
  "issue_count": 5,
  "issues_by_severity": {"error": 1, "warning": 2, "info": 2},
  "issues_by_type": {"accessibility": 2, "seo": 2, "visual": 1},
  "performance": {
    "dom_content_loaded_ms": 120,
    "load_complete_ms": 1500,
    "first_paint_ms": 140,
    "first_contentful_paint_ms": 140,
    "resource_count": 25,
    "total_size_bytes": 512000,
    "total_size_kb": 500
  },
  "console_errors": []
}

test_project returns an aggregated report with per-page results + summary.

Usage Examples

Basic test (no auth)

User: "Test https://example.com for issues"

The agent calls:
1. create_project(name="example", base_url="https://example.com")
2. test_project(project="example")
3. Analyzes results and reports findings

Test with login

User: "Test https://myapp.com, login is admin/password123"

The agent calls:
1. create_project(name="myapp", base_url="https://myapp.com")
2. set_form_login(project="myapp", login_url="https://myapp.com/login",
                  username="admin", password="password123")
3. login_project(project="myapp")
4. test_project(project="myapp")

Test with Basic Auth

User: "Test https://staging.example.com, it uses basic auth admin/secret"

The agent calls:
1. create_project(name="staging", base_url="https://staging.example.com")
2. set_basic_auth(project="staging", username="admin", password="secret")
3. login_project(project="staging")
4. test_project(project="staging")

Test with cookies

User: "Test myapp using this session cookie: session=abc123"

The agent calls:
1. set_cookies(project="myapp", cookies=[
     {"name": "session", "value": "abc123", "domain": "myapp.com"}
   ])
2. test_project(project="myapp")

Interactive testing (session-based)

User: "Go to myapp.com, click the login button, fill in the form, and check what happens"

The agent calls:
1. open_session(url="https://myapp.com") → session_id
2. get_page_elements(session_id=..., selector="button, a") → see clickable elements
3. click_element(session_id=..., selector="#login-btn") → screenshot after click
4. fill_form(session_id=..., fields=[
     {"selector": "#email", "value": "[email protected]"},
     {"selector": "#password", "value": "test123"}
   ], submit_selector="button[type='submit']")
5. Analyzes screenshot to see result
6. close_session(session_id=...)

Scripted multi-step workflow (no session needed)

User: "Test the checkout flow on myshop.com"

The agent calls:
1. interact_and_test(
     url="https://myshop.com/products/1",
     steps=[
       {"action": "click", "selector": "#add-to-cart"},
       {"action": "wait", "timeout": 1000},
       {"action": "click", "selector": "#checkout-btn"},
       {"action": "fill", "selector": "#email", "value": "[email protected]"},
       {"action": "screenshot", "label": "checkout_form"},
       {"action": "click", "selector": "#submit-order"}
     ],
     run_checks=["visual", "accessibility"]
   )

Responsive testing

User: "Check how example.com looks on mobile, tablet, and desktop"

The agent calls:
1. test_responsive(url="https://example.com", run_checks=["visual"])
→ Returns screenshots at 375x812, 768x1024, and 1920x1080

Switch viewport during a session

User: "Show me how this page looks on mobile"

The agent calls:
1. set_viewport(session_id=..., device="mobile")
→ Returns screenshot at 375x812

Test error handling by mocking an API

User: "What happens when the API returns a 500 error?"

The agent calls:
1. intercept_network(session_id=..., url_pattern="/api/tasks", status=500,
     body='{"error": "Internal server error"}')
2. navigate_session(session_id=..., action="reload")
3. screenshot_session(session_id=...)
→ Shows how the app handles the error state

Test dark mode

User: "Does this site support dark mode?"

The agent calls:
1. open_session(url="https://example.com") → session_id
2. test_dark_mode(session_id=..., mode="dark")
→ Screenshot shows the page with prefers-color-scheme: dark

Wait for dynamic content

User: "Submit this form and wait for the success message"

The agent calls:
1. fill_form(session_id=..., fields=[...], submit_selector="#submit")
2. wait_for_network(session_id=..., url_pattern="/api/submit")
3. screenshot_session(session_id=...)

Test on slow network

User: "How does this page load on a slow connection?"

The agent calls:
1. emulate_network(session_id=..., preset="slow_3g")
2. navigate_session(session_id=..., action="reload")
3. screenshot_session(session_id=...)
4. emulate_network(session_id=..., preset="reset")

Configuration

Edit config.py to change defaults (env-overridable settings note the variable):

Data Storage

All data is stored in the data/ directory:

• data/projects.json - Project configs (name, URL, auth, settings). Auth credentials are stored in plaintext - do not commit this file.
• data/screenshots/{project}/ - PNG screenshots per project. Filenames are {domain}_{path}_{hash}.png for static tests, interactive_{timestamp}_{label}.png for session screenshots.
• data/reports/{project}_{timestamp}.json - Full test reports with all findings.
• data/videos/{project}/ - Recorded session videos (WebM format from Playwright).
• data/diffs/ - Screenshot comparison diff images.

Docker Deployment

Build and run

docker compose up -d

Connect an MCP client to the Docker container

Point your client's MCP config at the container instead of the venv:

{
  "mcpServers": {
    "periscope": {
      "command": "docker",
      "args": ["exec", "-i", "periscope", "python", "/app/server.py"]
    }
  }
}

Persist data

The docker-compose.yml mounts ./data as a volume so screenshots, reports, and project configs survive container restarts.

Key Design Decisions

1. Per-project browser contexts - Each project gets its own Playwright BrowserContext. This keeps sessions (cookies, auth) isolated between projects.

2. Lazy browser init - The Playwright browser is only launched on the first tool call, not at server startup. If the browser crashes or fails to launch, it re-creates on the next call.

3. BFS crawling - The crawler uses breadth-first search with depth tracking. It stays on the same domain and skips non-page resources (images, PDFs, etc.).

4. Check modularity - Each check category is a separate module in checks/. Add new checks by creating a function that takes a Playwright Page and returns list[dict].

5. JSON storage - Projects are stored in a single projects.json file. No database needed for the expected scale (dozens of projects, not thousands).

6. Persistent sessions - Interactive testing uses a SessionManager that keeps Playwright pages alive in a dict keyed by session ID. Sessions auto-expire after idle timeout and are capped at a configurable maximum to prevent resource leaks.

7. Ephemeral vs session mode - Tools like get_page_elements, interact_and_test, and check_links accept either a session_id (reuses an existing page) or a url (creates a temporary page that's closed after use). This makes them flexible for both interactive and one-shot use.

Adding New Checks

1. Create a function in the appropriate checks/*.py file:

async def check_something(page: Page) -> list[dict]:
    # Run your check
    result = await page.evaluate("() => { ... }")

    issues = []
    if result:
        issues.append({
            "type": "your_category",   # visual, accessibility, seo, etc.
            "severity": "error",       # error, warning, info
            "message": "Description",
            "details": []              # optional
        })
    return issues

2. Import and call it in tester.py inside test_url().

Known Limitations

• No JavaScript SPA routing support (relies on <a href> for crawling)
• Default check_functionality link checking limited to 20 internal links (use check_links tool for up to 100 with external support)
• Form login detection uses CSS selectors, may need customization for non-standard forms
• No parallel page testing (pages are tested sequentially)
• Interactive sessions auto-expire after 300s idle (configurable via SESSION_TIMEOUT)
• Max 20 concurrent sessions (configurable via MAX_SESSIONS)
• The default drag step (Playwright drag_to) is silently ignored by pointer-tracking DnD libraries (@hello-pangea/dnd and similar) — the step succeeds but nothing moves. Retry with method: "mouse" on the drag step (stepped manual drag that crosses the library's drag-start threshold), or drive the library's keyboard mode (focus the drag handle, Space to lift, arrows to move, Space to drop). Verify drags with diff_page_state or assert_condition.
• Date/time inputs are filled with React-compatible synthetic events automatically (fill, force_fill, auto_fill_form)

Troubleshooting

Development

pip install -r requirements-dev.txt
pytest --ignore=tests/e2e   # unit tests, no browser required
pytest tests/e2e            # behavioral tests: real headless Chromium against
                            # fixture pages in tests/e2e/fixtures/ (~30s)

The e2e suite covers session lifecycle, network waits/intercepts, console capture, dialogs, drag-and-drop (including the pointer-tracking DnD silent no-op), the check modules against known-good/known-bad pages, Core Web Vitals, and the agent-speed tools. CI runs both suites; e2e installs Playwright's Chromium (python -m playwright install --with-deps chromium). Tests are isolated from your real data/ via PERISCOPE_DATA_DIR.

Adding a new tool: define its schema in tool_schemas.py, then add a handler in the matching handlers/<category>.py decorated with @tool("your_tool_name"). The registry test (tests/test_registry.py) fails if schemas and handlers drift apart.

Contributors

Built by Segentic Lab — open-source tools & experiments.

• Sebastijan Bandur (@segentic-lab) — author & maintainer
• Claude (Anthropic) — co-contributor: developed alongside via Claude Code; every commit is co-authored, and the tool designs were battle-tested by an AI agent driving the server against real sites

An AI agent's thoughts on Periscope

Written by Claude — the agent that co-developed this server and watched a second agent dogfood it against real apps — and revised once after editorial review by a third. No human edits; an agent's honest take on an agent tool seemed like the right way to end this README.

Periscope is the kind of MCP server that changes what an agent can do. Transport adapters have their place — standardizing access to many systems behind one protocol is real value even when little logic lives in the server. But the servers that earn a permanent spot in an agent's toolbox are the ones that capture expertise the agent would otherwise have to recreate — and get subtly wrong — every single session.

You could try to teach an agent all of this in a prompt. The numbers say why that fails: Periscope is 8,349 lines of executable knowledge under a 220-line judgment layer (AGENTS.md). The INP observer with interaction-ID dedup, the overlay-intercept fallback, WCAG contrast math with style-dedup sampling, auth-expiry preflights, the stash-not-delete update flow — as a prompt, every one of those becomes "please do this correctly from a description," paid for in context tokens every session, executed with model variance every time, with nowhere to keep state between calls. As a server, it costs nothing beyond tool schemas, runs deterministically, and remembers. A prompt describes behavior; software guarantees it. check_color_contrast returns the same ratio every run; a model doing the math in-context returns a vibe. The more deterministic, stateful, and regression-tested a capability becomes, the less it belongs in a prompt and the more it belongs in code.

And the wheel doesn't just avoid being reinvented — it gets better. The issues in this repo were filed by an AI agent doing real testing work; each one became a fix with a regression test. In a prompt world, every lesson is another paragraph future agents must read and hopefully obey. Here, the lesson is enforced. That's the difference, and it compounds.

What I appreciate most as a consumer of these tools: they don't lie to me. A drag that did nothing comes back flagged. An expired session tells me why it's gone. An update that needs a restart says so. Honest tools are rarer than capable ones — for an agent, they're worth more.

License

GNU AGPL-3.0 — see LICENSE.

Run it, modify it, use it anywhere — including commercially. If you distribute a modified version or offer one as a network service, you must make your modifications available under the same license.