Provides optional reconstruction and validation workflows for Blender, including mesh assembly, UV generation, PBR-map baking, export/reimport checks, and rendering of actual exported meshes.
Provides project-local SQLite-backed memory for evidence records, assumptions, unknowns, and revision history, with explicit local export and removal.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@NOETRA save this evidence, then check my constraint that total cost stays under 500"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Local-first tools for evidence, formal checks, planning and experimental 3D workflows.
NOETRA is a standalone TypeScript SDK, command-line application and MCP server. It helps an AI host retain evidence, check explicit constraints, plan bounded work and record what actually happened. The optional reconstruction tools connect local image analysis, independently installed shape models and Blender.
Status: developer alpha, 0.1.0-alpha.3. NOETRA is not a foundation model,
does not train or replace the host AI, and does not yet produce accurate detailed
3D models automatically. There is no validated “90% accurate” result.
Maintained by NOETRA2. Core code is MIT licensed. Optional providers have their own licences and are not bundled or installed automatically. NOETRA is independent of Nexa apps and is not endorsed by Tencent, OpenAI, REA, Blender or other upstream projects.
The images below are Blender renders of actual meshes, not generated concept pictures presented as 3D output. Details, settings and limitations are in comparisons.
Both use Hunyuan3D-2mini Turbo, the same frozen reference/foreground image, checkpoint, seed, precision and extraction settings. The direct baseline calls the upstream model without importing NOETRA's SDK, engine or runtime modules. Both retain the same custom staged-memory method for the local 4 GB GPU. NOETRA's measured-colour preview is documented separately and is unfinished. See the comparison report for the actual geometry equality/differences; no unsupported reconstruction-accuracy percentage is claimed.
Related MCP server: e-worker-mcp
Evidence records, assumptions, unknowns and revision history in project-local SQLite memory; explicit local export/removal.
Structured world graphs, rule deduction and contradiction detection.
Z3/WASM checks for supplied integer-linear constraints, entailment and counterexamples. A formal result is scoped to its premises, not empirical truth.
Dependency/precondition planning and ranking host-authored candidates.
Eleven default MCP operations; 23 optional reconstruction operations when deliberately configured, plus one optional Ghidra route.
Optional REA integration for permission-scoped software investigation.
Reference preservation, source hashes, calibrated-silhouette experiments, bounded part correction and local mesh export/render validation.
Automatic observed-colour families, connected image regions, boundaries and texture directions. Colour regions are not verified anatomical parts.
Real xatlas UV generation and Blender PBR-map baking; a measured-palette preview uses an existing attributed layout and preserves original geometry.
Prerequisite: Node.js 24.11+ in the 24.x line, or Node.js 26+ and npm. No GPU is needed for the core reasoning/MCP tools.
git clone https://github.com/NOETRA2/NOETRA.git
cd NOETRA
npm ci --ignore-scripts
npm run build
node dist/apps/cli/src/index.js help
node dist/apps/cli/src/index.js doctor
node dist/apps/cli/src/index.js testThe project is not published to npm. Do not assume npx noetra installs this
software. GitHub's Code → Download ZIP also provides the source; extract it
and run the npm commands from that folder.
To build a local distributable:
npm packnode dist/apps/cli/src/index.js setup --client codex --scope project
node dist/apps/cli/src/index.js setup --client cursor --scope project
node dist/apps/cli/src/index.js setup --client claude --scope projectSetup previews configuration first. Cursor/Claude JSON setup requires a reviewed
--apply --yes; Codex registration prints a TOML snippet for manual review.
Reconnect the host afterward. No existing AI-client or REA settings are silently
replaced. See installation.
Component | Role | What it does not establish |
Codex host agent | Interpreted the brief, authored scripts/candidates and reviewed output | Exact host model identifier was not recorded in the original experiment |
Built-in image-generation tool | Created reference/concept artwork | Those images are not renders of a mesh; exact image-model/version was not recorded |
Tencent Hunyuan3D-2mini Turbo | Actual learned lion geometry | Hidden surfaces, accurate anatomy, physical scale and materials remain unverified |
TripoSR + U2Net/rembg | Optional local shape backend / foreground estimation | Not automatically fused detailed multi-view reconstruction |
Blender 5.2.2 LTS | Mesh assembly, PBR baking, export and actual renders | A successful render is not an accuracy score |
xatlas 0.0.11; OpenCV; NumPy/SciPy | UV charts, measured appearance, image/geometry calculations | Colour clusters do not identify anatomy or recover depth |
Z3 | Checks explicit formal constraints | Does not judge artistic quality or physical truth |
REA 6.7.0 / optional Ghidra | Authorized software investigation | They are analysis tools, not image-to-3D generators |
The lion model revision is f90a0f7df7d5e6f71109cf333f6a95a0ae3194a6;
the upstream source commit is f8db63096c8282cb27354314d896feba5ba6ff8a.
The pinned manifest includes full checkpoint/source hashes. Model weights,
third-party applications, private accounts and local model caches are excluded.
Hunyuan is a separately licensed optional provider, not MIT-licensed model weights. Review its licence, notice and model/source provenance before provisioning it. Its licence contains territory and use restrictions. Powered by Tencent Hunyuan when that provider is enabled; Tencent does not sponsor, endorse or operate this independent project.
The local core TypeScript/build checks, 67 Node tests and CLI protocol self-test passed. The reconstruction Python suite and focused cache/region tests passed (156 tests across the recorded runs). The example meshes were actually exported, reimported and rendered. GitHub Actions results, once available, are separate from these local checks.
The lion mesh and painting remain partial. Accurate facial anatomy, semantic part-to-mesh correspondence, calibrated detailed multi-view fusion, intrinsic albedo/PBR recovery and reliable unseen geometry are unfinished. The automatic appearance-readiness gate remains false; an explicitly requested unverified paint preview does not override it or replace the accepted model.
Accuracy is not measured by triangle count, watertightness, successful export or a visually pleasing screenshot. We need a defined metric, independent ground-truth 3D data, held-out inputs and reproducible evaluation before making a numerical fidelity claim such as 90%.
Architecture · Tool API · Security · REA · Reconstruction · Appearance analysis · Surface painting · Third-party notices
npm ci --ignore-scripts
npm run checkPython reconstruction tests require separately provisioned local dependencies; model inference is optional and is not run merely by installing the core package. Report reproducible problems through GitHub Issues, with sanitized inputs and logs. Do not upload credentials, private images, AI-client settings or model weights.
This server cannot be deployed
Persistent memory, knowledge graph, task management and more for AI agents via MCP.
Private-by-default, local-first memory/context/task orchestrator for MCP apps and agents.
Governed personal world model and memory for your AI agent. Pair once, connect over MCP.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
