ghostlab 0.2.1

Local end-to-end testing lab for any MCP server: coding agents role-play real users, drive your tools over multiple turns, score runs, and render/interact with MCP Apps ui:// widgets.

Ghostlab

A local, end-to-end testing lab for any MCP server — coding agents role-play real users, drive your tools over multiple turns, and the harness captures traces, scores outcomes, and even renders and clicks through MCP Apps UI widgets.

Test your MCP server the way it's actually used — not with unit tests against the protocol, but with a real coding agent (Codex / Claude) that picks tools, makes mistakes, and tries to accomplish goals, while a second agent plays the user. Protocol-level checks (schema errors, a tool call that 500s) are useful sanity checks, but they aren't the real test — the real test is whether an agent can actually get a task done through your MCP, end to end.

📖 Docs wiki: https://sajjadgg.github.io/Ghostlab/ · 🤖 For agents: llms.txt · 🛠 Contributing: CONTRIBUTING.md

Naming: the project and repo are Ghostlab (formerly Rehearsal). The CLI is ghostlab, with rehearsal kept as an alias, and the installed Python package is rehearsal — all the same project.

Quickstart

python3 -m venv .venv                # Python 3.10+
.venv/bin/pip install -e .            # add '.[ui]' for the web UI, '.[apps]' for widget rendering

ghostlab create

That's the whole flow. ghostlab create walks you through everything, end to end:

1. Name + target — the only two prompts. A target is an MCP URL, or a path to a target JSON / standard mcpServers config.
2. Discover — connects to the target, lints its contract (schema errors, risk labels), and probes any MCP Apps ui:// widgets.
3. Configure semantic testing — if codex is on your $PATH, offers to wire it up as the agent-under-test: a real coding-agent session with your MCP's tools available, so the dual-agent conversations below have something to actually drive. (No codex? The wizard still finishes — protocol-level suites still run, semantic/security just skip until a host is configured; see Runner Configs.)
4. Generate a test plan — personas × scenarios for the semantic/security suites, plus deterministic coverage for every discovered tool (test-plan.yaml), all editable afterward.
5. Pick which suites to run — defaults to everything; narrow it to just semantic while you're iterating, or the full set for a release check.
6. Run + review — executes the plan against your configured host(s), writes a colored pass/fail summary plus a dashboard, and prints the readiness/gate verdict.

Everything the wizard does is one of discover / plan / test / review under the hood — run any of them standalone afterward to iterate without repeating the whole wizard:

ghostlab discover --job <name>    # re-inspect after the target changes
ghostlab plan --job <name>        # regenerate/curate the test plan
ghostlab test --job <name>        # rerun (add --suite semantic to narrow it)
ghostlab review --job <name>      # the readiness/gate report on its own

A job is a self-contained folder: jobs/<name>/job.yaml (target, hosts, generation/test defaults, gates — all editable), test-plan.yaml, workspace/ (discover/generated/test artifacts + a local sqlite db), and runs/.

What you get

Stage	What it produces
Discover	A deterministic contract.json (schema lint, risk labels, MCP Apps metadata checks) and a refreshed capabilities: section in job.yaml
Plan	A coverage-driven test-plan.yaml: deterministic protocol cases for every tool, plus generated persona/scenario cases for the semantic/security suites
Test	Multi-host execution results (results.json/results.md), a standalone HTML dashboard, and — for conversational cases — full dual-agent transcripts with structured tool-call capture
Review	A readiness report: pass/fail gate verdict, failure clusters, and prioritized repairs

See it in action

Watch a real coding-agent drive your MCP, turn by turn — every tool call is captured with its pass/fail status. Below is the live trace of a Hugging Face MCP run, including two hf_hub_query calls that failed against the server:

Get a standalone HTML dashboard — pass rate, per-case verdicts, and suite/host tags at a glance:

Drill into any case — the goal and persona, the judge's verdict with its reasoning, and the full dual-agent transcript with inline tool calls:

Goal

Build a repeatable, sandboxed tester that can:

• Run any target MCP app in an isolated environment.
• Launch one coding-agent session as the agent-under-test (with target MCP injected).
• Launch another coding-agent session as the user emulator (persona + goal driven).
• Drive multi-turn interactions between them.
• Capture full traces, tool activity, failures, and outcomes.

This lets you test with your existing Codex/Claude usage path, instead of wiring a separate LLM provider deployment just for E2E testing.

Scope

Ghostlab is intentionally app-agnostic:

• Works with any MCP server reachable by stdio/SSE/streamable HTTP.
• Supports local or remote MCP endpoints.
• Supports multiple coding-agent runners (Codex, Claude Code, and future adapters).

No Cortex-specific assumptions are required in the core harness.

Reference

Everything below is the individual-command reference and advanced usage — useful once you're past the first ghostlab create run, or scripting CI.

Spec vs job

There are two ways to hold an evaluation's config; for almost everyone the answer is a job.

• Job (recommended) — a self-contained jobs/<name>/ folder created by ghostlab create. Every command takes --job <name> (or auto-detects job.yaml in the current dir). This is the mainstream path the whole Quickstart uses.
• Spec (advanced) — a single standalone ghostlab.yaml produced by ghostlab init, addressed with --spec <file>. Useful for scripting or keeping config outside the jobs/ layout. Unless you specifically need that, prefer a job.

The commands overlap (discover/plan/test/review accept either --job or --spec); pick one model per evaluation and stay with it.

Job folder layout

jobs/<name>/
  job.yaml          # target, setup, hosts, generation, test, prompts, gates
  test-plan.yaml    # produced by `ghostlab plan`
  workspace/        # discover/, generated/, test/ artifacts + ghostlab.sqlite3
  runs/             # dual-agent run output

Core dual-harness architecture

1. AUT Harness (Agent Under Test) — starts a coding-agent session (Codex or Claude Code), injects the target MCP server config into it, and exposes a controlled I/O bridge so it can receive user messages and return replies/tool results.
2. User Emulator Harness — starts a second coding-agent session, gives it a scenario file (persona, goals, constraints, success criteria), and asks it to act like a realistic user, sending messages turn-by-turn to the AUT.
3. Orchestrator — coordinates turn-taking, timeouts, retries, and stop conditions; logs every message/event in structured format; produces a run report with bug candidates and reproduction context.

Target configuration model

Each test run points to a target definition:

• target.id: unique name (filesystem-mcp-local, my-app-staging)
• transport: stdio | sse | streamable-http
• connection: command+args+env (stdio) or URL+headers (network transports)
• capabilities: optional expected tools/resources/prompts
• startup: optional health checks and boot timeout

Commands

The package installs two equivalent console scripts: ghostlab and rehearsal.

• ghostlab create — the end-to-end wizard described above.
• ghostlab init — advanced: scaffold a standalone ghostlab.yaml spec from a target JSON (see spec vs job — most users want ghostlab create).
• ghostlab discover — inspect the job's target, lint its contract, refresh capabilities.
• ghostlab plan — generate (or curate) the coverage-driven test plan.
• ghostlab test — execute the test plan across the job's host adapters.
• ghostlab review — readiness report over discover + plan + test artifacts (release gate).
• ghostlab inspect — connect to a target MCP and capture what it exposes (no job needed).
• ghostlab profile — turn an inspect.json into a capability profile (codex).
• ghostlab generate-scenarios / generate-personas / generate-dataset — build reusable persona×scenario datasets outside the job model.
• ghostlab review-dataset / run-dataset — curate and run a standalone dataset.
• ghostlab run — run one dual-agent E2E scenario directly.
• ghostlab evaluate — score a run into a pass/fail verdict (codex judge).
• ghostlab critique — rate a run's tool ergonomics from the agent's perspective (codex).
• ghostlab scorecard — roll run verdicts and critiques into a summary scorecard.
• ghostlab compare — diff two dataset runs for regressions.
• ghostlab apps-probe / apps-render — probe/render MCP Apps ui:// widgets.
• ghostlab doctor — check codex and validate runner presets.
• ghostlab dashboard — build a standalone HTML dashboard for a ghostlab test run.
• ghostlab ui — launch the Streamlit pipeline UI.
• ghostlab db — manage the SQLite persistence database.

The UI: ghostlab ui

Run the whole pipeline from a browser instead of the CLI:

pip install 'ghostlab[ui]'       # installs streamlit
ghostlab ui                      # opens http://localhost:8501

The app mirrors the same job-based flow as ghostlab create: pick or create a job, discover its target, configure the agent-under-test host, generate and curate the test plan, run selected suites, and review colored results and full conversation traces — the same job.yaml/test-plan.yaml/results.json a CLI run of the same job would produce.

Understand a new MCP: inspect

Point it at a target and it introspects the server without any coding-agent credits or manual curl:

ghostlab inspect --target examples/target.json

This connects over the configured transport (stdio / streamable-HTTP / SSE), runs the initialize handshake, and pages through tools/list, resources/list, resources/templates/list, and prompts/list. It writes runs/<id>-inspect/inspect.json (raw) and inspect.md (readable), and lints tool/resource descriptions for references to tools the server does not actually expose. This capability dump is the input to capability profiling and scenario generation.

Profile a new MCP: profile

Turn the raw inspect.json into a structured capability profile — the bridge between Understand and Generate. Deterministic structure (tool taxonomy by name family, read/write state surfaces, gaps) is computed locally; a domain summary and inferred multi-step workflows are generated by codex:

ghostlab profile \
  --inspect runs/<id>-inspect/inspect.json

It writes capabilities.json + capabilities.md next to the inspect.json. Generated workflow steps are filtered to real tool names, so the profile never references hallucinated or non-exposed tools.

Generate scenarios: generate-scenarios

Generate grounded use-case scenarios the MCP supports, derived from the capability profile:

ghostlab generate-scenarios \
  --profile runs/<id>-inspect/capabilities.json \
  --n 3 \
  --output-dir scenarios

Scenarios are spread across intents (happy_path / edge_case / adversarial) and each declares an exercises list of the tools it should drive the assistant to use. Tool references are filtered to real tool names.

Build a persona library: generate-personas

Personas are reusable user profiles decoupled from scenarios, so the same persona can be paired with many scenarios (the basis for the dataset matrix). Generate a domain-relevant library from a capability profile:

ghostlab generate-personas \
  --profile runs/<id>-inspect/capabilities.json \
  --n 4 \
  --output-dir personas

Each persona has a summary, behavioral traits (terse, impatient, easily confused, non-native, ...), and a domain context map (native_language, target_exam, level, ...). Pass one to a run with --persona:

ghostlab run ... --persona persona.json

Build a dataset: generate-dataset

A dataset is a persona x scenario matrix — different users, and different scenarios tailored to each of them. For every persona, codex generates persona-specific scenarios, and the pairs become runnable cases:

ghostlab generate-dataset \
  --profile runs/<id>-inspect/capabilities.json \
  --personas 3 --scenarios-per-persona 3 --seed 7 \
  --name cortex

This writes a self-contained dataset directory:

datasets/cortex/
  dataset.json          manifest: mcp, seed, cases[]
  personas/<id>.json
  scenarios/<id>.json    persona-namespaced; inline `persona` is a situational note

Review & curate a dataset: review-dataset

Before spending agent credits, check that the dataset makes sense:

ghostlab review-dataset \
  --dataset datasets/cortex \
  --profile runs/<id>-inspect/capabilities.json

This writes review.md + review.json with a tool-coverage matrix, per-case previews, and flags (near-duplicate cases, scenarios exercising non-exposed tools, personas with no scenarios). Curation is file-first — each case gets a status in dataset.json (pending / approved / rejected / needs-edit):

ghostlab review-dataset --dataset datasets/cortex \
  --approve case-a case-b --reject case-c

Run a dataset: run-dataset

ghostlab run-dataset \
  --dataset datasets/cortex \
  --target target.json \
  --aut-runner runners/codex-cortex-aut.json \
  --user-runner runners/codex-user-emulator.json \
  --limit 2

Each case runs through the orchestrator (with its persona) into its own run directory, and a dataset-level summary.md + results.json capture per-case status and turn counts.

Tool-call capture & output hygiene

Every run captures structured MCP tool calls from the agent host. The codex AUT runners set "parser": "codex-json" and run codex exec --json, so the orchestrator parses the JSONL stream and records each mcp_tool_call with its arguments, result, error, and status into events.jsonl, with a per-turn table in report.md. stdout and stderr are kept separate: only stdout (with known host noise redacted) becomes the conversational message handed to the other agent, while raw stderr is logged for debugging.

Evaluate a run: evaluate

ghostlab evaluate --run runs/<id> --capabilities runs/<id>-inspect/capabilities.json

Combines deterministic checks over captured tool calls with a codex LLM-judge that scores each success_criterion/failure_signal from the transcript. Writes verdict.json + verdict.md; exits non-zero unless the verdict is pass.

Compare two runs: compare

ghostlab compare --base runs/<base>-summary --candidate runs/<cand>-summary \
  --output comparison.md

Diffs case-by-case on verdict, listing regressions first, then fixes, then other changes. Exits non-zero when there are regressions, so it can gate CI.

MCP Apps: apps-probe / apps-render

Some MCPs ship MCP Apps UI resources — a tool's _meta.ui.resourceUri points to a ui://… HTML widget a compatible host is expected to render.

apps-probe connects to a target, finds every UI-producing tool, fetches each ui:// resource, and reports render-readiness and CSP diagnostics:

ghostlab apps-probe --target target.json

apps-render actually renders a ui:// widget in headless Chrome, proving a user can see and use it — it implements the MCP Apps host bridge, mounts the widget in a sandboxed iframe, completes the ui/initialize handshake, feeds it real tool input/result, and can drive a sequence of UI intents:

pip install 'ghostlab[apps]' && playwright install chrome    # one-time
ghostlab apps-render --target target.json \
  --tool views_generate_sentence_scramble \
  --arguments '{"target_sentence":"The cat sat on the mat","shuffled_elements":["mat","The","on","sat","cat","the"]}' \
  --intent '{"type":"reorder","value":["The","cat","sat","on","the","mat"]}' \
  --intent '{"type":"reveal"}'

It writes apps-render.json + apps-render.md, a widget.png of the initial render, and widget-final.png after the intents run. Exit status is non-zero if the render errored or any assertion failed.

Session runner (one live agent across turns)

By default each turn spawns a fresh agent process and the orchestrator replays the transcript. The session runner ("kind": "codex-session") instead keeps one codex session alive: turn 1 records the thread_id, and later turns run codex exec resume <thread_id> so codex retains context — fewer tokens, no repeated cold-start noise.

ghostlab run --target target.json --scenario <scenario.json> \
  --aut-runner runners/codex-cortex-local-session.json --user-runner <user.json>

Validate your setup: doctor

ghostlab doctor               # validates runners/*.json
ghostlab doctor --runners runners/codex-cortex-local-session.json

Reports the codex binary + version and validates each runner's kind, command, and parser.

Default agent backend

codex is the default coding-agent backend for generation, the agent-under-test, and judging. inspect needs no agent — it is a direct MCP client. The codex binary is auto-detected from $PATH, then the macOS app bundle (/Applications/Codex.app/Contents/Resources/codex); override with $REHEARSAL_CODEX_BIN or --codex-bin.

Colored output

CLI output is colored automatically on a TTY (dual-agent transcripts, pass/fail/skip verdicts, gate failures). Set NO_COLOR=1 (or GHOSTLAB_COLOR=0) to disable it, GHOSTLAB_COLOR=1 to force it on (e.g. piping into a pager that groks ANSI).

Runner Configs

Mock runner (no agent, free):

{ "kind": "mock" }

Process runner:

{
  "kind": "process",
  "command": ["codex", "exec", "-"],
  "env": {},
  "timeout_seconds": 300,
  "prompt_mode": "stdin"
}

The process runner starts one fresh process per turn. prompt_mode can be stdin, append-arg, or replace-placeholder. ghostlab create synthesizes one of these automatically for the agent-under-test host (wiring the target MCP in via codex's -c mcp_servers.<id>... overrides, including bearer_token_env_var for Bearer ${VAR}-style auth headers) — see jobs/<name>/runners/aut.json after running it. To use Claude Code or another agent as the AUT instead, hand-write a runner JSON (see runners/claude-process.example.json) and pass it via --aut-runner to ghostlab create/ghostlab plan, or add it directly under hosts: in job.yaml.

Install from PyPI

pip install ghostlab            # add [ui] and/or [apps] for those extras
ghostlab --help

Packaging & Release

Build and validate distributions locally:

.venv/bin/python -m pytest
.venv/bin/python -m build
.venv/bin/twine check dist/*

CI runs tests on Python 3.10 through 3.13 and verifies that the package builds. Releases are automated: the publish.yml workflow builds the sdist + wheel, publishes them to PyPI via Trusted Publishing, and attaches them to the GitHub Release — triggered when you publish a GitHub Release (or run the workflow manually). Cut a release like:

# bump rehearsal/__init__.py __version__ first, then:
gh release create v0.1.0 --generate-notes

To enable publishing, create the PyPI project ghostlab and add a Trusted Publisher for this repository, workflow .github/workflows/publish.yml, environment pypi. No PyPI username or token is committed.

The Pages workflow builds the docs wiki with MkDocs and deploys it to GitHub Pages on pushes to main, v*.*.* release tags, and manual workflow runs. In the GitHub repository settings, set Pages to use GitHub Actions as the source.