Code Factory MCP Server

Code Factory

Catch AI-generated tests that could never fail — before review.

One command answers one question: can this test actually fail? Code Factory runs a safe negative control, records what happened, and gives you a local result you can review. It challenges whether a test can actually reject the failure it claims to cover. It does not upload your code or call a green test proof when that test could never catch the failure.

pip install factoryline-code-factory
factory first-proof --root .

That one local command creates a disposable demonstration sandbox, proves the healthy check can pass, then proves the hollow check is rejected. It writes a receipt and a privacy-safe Proof Card; it does not assess or change your project.

Pick the path that matches your work

• Individual developer or vibe coder: start with factory first-proof. Learn what a real negative control looks like before trusting AI-generated tests.
• Engineering team: put factory wrap around an admitted Codex, Claude Code, DeepSeek, or other local agent run. Code Factory records the exact file delta, runs the validators your team declared, and stores hashes and bounded facts—not prompts or raw model output—so reviewers can compare the claim with the change.
• SaaS builder: use factory saas verify to follow one customer promise from OAuth/OIDC identity and tenant access through checkout, verified webhook, entitlement, feature access, and revocation. Missing or contradictory evidence stays blocked, and the provider remains untouched.
• App builder: use factory revenue appforge-design to turn confirmed user intent into a story-led iOS storyboard, then run factory revenue app-review-gate before submission. Thirty exact-build policy and regression gates surface avoidable purchase, restore, iPad, screenshot, privacy, accessibility, metadata, reviewer-access, and export gaps while there is still time to fix them. It is designed to save days of review/rework waiting and minimize avoidable rejection risk; it cannot guarantee Apple approval.
• Platform or assurance team: evaluate local policy gates, evidence packets, expiring exceptions, and tenant boundaries in a controlled pilot. The free core is community-supported on a best-effort basis; the proposed enterprise support SLA is documented separately and is not effective until its activation evidence and signed terms exist.

When you need more than the first proof, open the advanced workflow guide. App and SaaS builders can open the standalone AppForge App Review Gate guide, or use the same receipts inside Code Factory Graph Ops, MCP, WebMCP, and supported IDE adapters. For one agent-neutral human review path, use factory proof-review to seal intent, audit trajectory evidence, route the change, learn confirmed regressions, inspect the team inbox, and export a tamper-evident Proof Card. To create a contained starter later, run factory mvp "Build an approval tracker" --root .. That starter is never called production-ready until the relevant proof exists.

AppForge: make the review queue the last surprise, not the first

You have a build, a launch date, and a review queue that can take days to come back around. The costly failure is often not a crash—it is discovering after submission that the selected build has no exercised restore path, an iPad dead end, unrepresentative screenshots, an accessibility gap, or reviewer access that does not match the app.

AppForge turns that uncertainty into a local, candidate-bound worklist. Start with the user’s design direction, keep a story-led storyboard, then bind real iPhone/iPad media, App Review checks, quality evidence, SaaS journey evidence, and a final Markdown/PDF dossier to the same build. In JetBrains, AppForge Mission Control makes the missing lane visible before a human decides to enter the queue.

That can prevent avoidable rework and repeat review cycles; it does not submit to Apple, access credentials, replace TestFlight, provide legal advice, or guarantee approval. Start with the AppForge Evidence Kit.

Support posture

The open-source core is free. Community support runs through public GitHub issues and has no guaranteed response time. For teams that need an operating commitment, the repository includes a transparent proposed Support SLA with P1/P2/P3 response targets, availability, recovery, exclusions, and an activation checklist.

Those targets are planning terms, not a current contract: the SLA remains proposed and non-effective until Code Factory has a named support owner, monitored escalation, production telemetry, dependency and recovery evidence, legal/security terms, and a signed enterprise order. This keeps the GitHub page useful to evaluators without implying that a free repository already carries a managed-service guarantee.

See actual Factory Studio and the current FactoryLine visual set or open the live Hugging Face Space.

Advanced workflows

• Start a real project. Turn one outcome into a contained web, mobile, API, CLI, worker, MCP, or agent-UI starting state.
• Review what AI produced. Turn requirements, diffs, proofs, and blockers into receipts, Graph Ops, and one fact-derived next action.
• Refresh the evidence that matters. Watch a local Assembly while it runs, refresh the next-proof brief, and see privacy-bounded observed Git contribution context without pretending it is a verified directory or billing roster.
• Keep "done" honest. Challenge declared validators for hollow tests; a green-looking scaffold is never called production-ready by default.
• Stop vague work before it starts. Use Intake Grill to record framework, exact intent, observable acceptance evidence, and the external-effects boundary before a Product Mission begins.
• Make a behavior survive its declared failure cases. Use the supervised Gauntlet to turn human-written E2E sabotage cases into an offline-verifiable Survival Card. A hollow negative check stays visible; a card is never a production-readiness certificate.
• Let autonomy be earned, not assumed. Use factory license to derive an expiry-bound local tier from independently verified governed runs. A severe hollow-test, hollow-validator, or scope-escape result demotes the declared agent automatically; it never authenticates identity or grants execution.
• Compare agents with receipts, not vibes. Use factory combine to rank completed, sealed, independently verified runs on the same task. It never starts an agent or turns a local scoreboard into a vendor-quality claim.
• Make every agent session feed the evidence loop. Use factory wrap around Claude Code, Codex, DeepSeek, or another local CLI. It verifies a sealed admission before launch, observes the exact file delta, runs declared independent validators, and records the result for Agent License and Combine. It stores hashes and bounded facts—not prompts or raw output—and observes the process without pretending to sandbox it.
• Keep decisions from becoming tribal knowledge. Use factory judgment to track a human-promoted design decision with exact paths, a named owner, review date, and hash-bound proof obligations. A Change Safety Case routes one explicit diff to its owner; it never infers intent, promotes a decision, runs a test, or approves a release.
• Prove the whole journey—and audit the repairing agent. Use factory journey to compare declared and observed product journeys, preserve a hash-bound failure capsule, prove state flow and cleanup, and challenge a proposed healing with a negative mutation. Choose human-controlled or bounded supervised-auto mode; every autonomous attempt receives a separate FactoryLine audit and neither mode can self-approve.

What the latest upgrade resolves

These are local evidence and supervision tools, not guarantees of performance, security, productivity, production readiness, or an automatic repair service.

FactoryLine by role

Expected operational outcomes

FactoryLine is designed to reduce avoidable review loops, evidence hunting, and the frustration of discovering late that an AI-generated test never exercised the claimed behavior. The expected outcome is a shorter path to a defensible human decision—not a promise that every project becomes faster or cheaper.

FactoryLine never converts an unmeasured estimate into a savings claim. Use the Savings Tracker to report observed time or cost only when the repository has a valid baseline and evidence window.

For planning, a transparent starting band is 25–120 minutes of avoidable review and rework per AI-assisted PR: roughly 10–30 minutes of reviewer context reconstruction plus 15–90 minutes of one preventable clarification, validator, or scope-rework loop. At 50 AI-assisted PRs per month, that models about 21–100 hours; at a team-supplied loaded cost of $75–$150/hour, the illustrative range is about $1,560–$15,000 per month. These are adjustable scenario inputs, not observed FactoryLine results. See the outcomes and savings model before using the range.

Works with your existing AI development stack

FactoryLine is the proof and control layer around generation, orchestration, and review tools. The status column distinguishes implemented adapters from clean workflow fits; it does not imply a vendor partnership.

See the complete compatibility and handoff guide for the recommended flow and the exact authority boundary for each stack.

For teams: use the Teams and Enterprise Operations Manual to run the same proof-first loop with named reviewers, approved AI-change scope, and explicit Proof Debt—without giving Code Factory merge, release, or provider authority. The commercial packaging guide keeps the free core separate from proposed Team and Enterprise services that are not purchasable yet. For a human-selected, customer-managed reference pilot, the local Team Pilot readiness gate hash-binds the operating evidence for owner review; it does not accept a customer or activate a paid service.

Design is part of the review. For UI-scoped work, add the optional Prestige Design Review: a purpose-led design brief plus review artifacts for hierarchy, responsive behavior, affordances, consistency, and declared design tokens. It makes design quality visible; it does not claim a conversion result, WCAG certification, or production readiness.

Reuse a proven decision without reusing stale context. Factory Continuity keeps a local, purpose-bound record of the evidence behind prior work. Graph Ops can replay only redacted, current, independently promoted metadata; it does not store private source, prompts, embeddings, or transcripts, and it cannot execute a repair.

Revenue-ready iOS scaffolds

factory revenue build turns one reviewed products.yaml into StoreKit 2, a transparent SwiftUI paywall, an Apple-JWS-verifying entitlement-server scaffold, privacy/review artifacts, and a local evidence page. Then replay, testflight-sync, failure-matrix, and policy-watch expose where observed purchase behavior, beta feedback, negative paths, or policy sources diverge—without turning unknowns green. Evidence Memory carries human-approved, receipt-backed lessons into the next exact-app journey while expiring stale knowledge and quarantining contradictions. IDEs and coding agents can inspect the same local facts through MCP; compatible browser agents can inspect bounded Graph Ops status through progressive WebMCP. App Store writes, pricing, experiments, offers, publication, and credentials stay human-controlled. See RevenueForge and the MCP/WebMCP handoff.

Install

# No account, model key, or cloud connection is required for this local run.
pip install factoryline-code-factory
factory first-proof --root .
factory mvp "Build an approval tracker" --root .
factory studio --root .\my-mvp

If Code Factory helps you find a proof gap or makes an AI-assisted change easier to review, star Code Factory so other developers can find it. This optional link only opens the repository.

Choose the job in front of you

For the short product map, read the overview. For a two-minute first run, follow Start Here. For full command and contract reference, browse the documentation directory.

Use Code Factory with CodeRabbit or another AI reviewer

They solve different parts of the review problem. CodeRabbit can supply AI findings and suggestions; Code Factory makes declared local proof gaps, coverage, and the next review action explicit. Enable the opt-in GitHub Proof Review workflow to put one neutral, commit-bound FactoryLine Check and walkthrough beside existing CodeRabbit comments. It uses no CodeRabbit account, API, credential, or output as proof.

For agent-created pull requests, add a small human-approved factory.agent_plan.v1 envelope. Plan-to-Proof compares that plan with the exact diff and exposes Proof Debt: unresolved scope, declared-test, human-routing, and existing proof obligations that must be settled before a team makes its own merge decision.

That makes Code Factory a standalone proof layer for teams that do not use an AI reviewer, and a complementary evidence layer for teams that do. For agent-created pull requests, Code Factory does not replace human review, auto-approve a pull request, or merge code.

Use Code Factory with LangGraph

LangGraph teams can keep their own graph and checkpoint runtime, then use the LangGraph Assurance Bridge to compare recorded reference and resumed transitions. The free local adapter produces hash-only parity evidence and a reviewable incident capsule when a run diverges; it does not import LangGraph, invoke a graph, or claim production resilience. The optional GitHub Action puts the same Proof Card in a pull-request job summary.

For Codex, Claude Code, and Deep Agents, install the Code Factory LangGraph plugin to add the proof workflow and read-only local MCP facts to the coding-agent surface.

Use Code Factory with DeepSeek Harness

The optional DeepSeek Harness adapter starts the same local read-only MCP proof surface through Harness's official generic MCP client. It lets a Harness agent inspect Graph Ops, current proof gaps, and Earned Autonomy / Combine facts without sending source to a hosted endpoint or gaining permission to execute, repair, approve, release, deploy, sign, or use credentials.

Use it where you work

Code Factory keeps the same local, receipt-bound workflow across the command line, VS Code, and the JetBrains plugin. It also provides local stdio Cursor or OpenCode MCP without handing an AI client permission to publish, deploy, approve, sign, or access credentials.

The same local proof surface is discoverable in the Official MCP Registry as io.github.zrk222/code-factory; registry setup starts the public PyPI package over local stdio and never creates a hosted service or write authority.

FactoryLine's core local proof workflow remains free. The planned JetBrains Freemium transition starts January 1, 2027, subject to Marketplace and activation gates. Early adopters may retain Founding Proof Pro at $5.95 USD/month or $60 USD/year; standard Proof Pro is planned at $9/month or $90/year, and AppForge Builder at $24/month or $240/year. Team Assurance is a separate $20-per-active-contributor annual-commit hypothesis. None is active today; no checkout, entitlement, or license enforcement exists. See the Marketplace control-room guide for the exact feature boundary and approval gates.

The matching GitHub Assurance Seat is also planned for January 1, 2027 at the same future price. It is for maintained, customer-managed proof operations (commit-bound review, Proof Debt, policy drift, governed exceptions, and evidence packets)—not source access or opaque AI-token resale. The source license and free core are unchanged. See the GitHub per-seat plan.

For Open VSX, the extension and local proof core remain free. All capabilities shipped before the transition are free through December 14, 2026. From December 15, 2026, Founding Proof Pro may remain $5.95/month or $60/year for early adopters; standard Proof Pro is planned at $9/month or $90/year, AppForge Builder at $24/month or $240/year, and Team Assurance at $20 per active contributor/month annually or $24 monthly, subject to explicit activation gates. See the Open VSX service plan.

The proof boundary

Code Factory creates and inspects local artifacts. It does not silently call a model, discover credentials, publish, deploy, sign, approve, message, or grant a connector. The Gauntlet executes only caller-declared E2E pairs after a named, expiry-bound, one-run admission; all other Gauntlet paths are read-only. Its deterministic proof receipts bind supplied byte bindings, declared identities, and evidence; an external runner must separately prove runtime isolation and network policy. Token, cost, and productivity claims remain unknown until a bound measurement exists.

flowchart LR
  intent["Plain-language outcome"] --> mvp["Local MVP"]
  mvp --> evidence["Receipts and declared checks"]
  evidence --> review["Graph Ops / review packet"]
  review --> decision["One evidence-backed next action"]

Use Intake Grill to pin down intent before a mission, PRD Grill before code exists, the deterministic contradiction gate when requirements collide, Proof Review when a diff arrives, and the Verifier Plane when a worker claims it is finished. Use the supervised Gauntlet when a specific behavior needs to survive explicitly reviewed failure cases. The local MCP contract and generated Mermaid output map make the same proof context reusable by a client you choose.

Go deeper when you need it

• Read why I built Code Factory for the founder story behind catching passing tests that fail in real use.
• Start with PRD Grill, Proof Review, or Verifier Plane when the job calls for it.
• Browse Graph Ops, Gauntlet, Proof-Delta Loop, Graph Portfolio and Run Admission, Evidence Frontier, Factory Reality Check, proof reuse, and LangGraph Assurance, and savings boundaries for advanced evidence workflows.
• Use IDE Health and Index Continuity when a JetBrains IDE slows down and you need locally observed signals before deciding what to review.
• For UI work, read Prestige Design Review for the optional design-quality lane and its explicit review boundaries.
• Read The approval signal decays when AI-written code becomes routine for the design and limits of the habituation gate.
• See the 0.45.3 release notes, CHANGELOG.md, release channels, and publication guide for versioned release detail.

License

MIT OR Apache-2.0.