Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Runs on your computer
atoma
Overview
Description from the maker (original English)
Start and follow atoma builds; read their runs, traces, agent registry and skill catalogue.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi3 of 3 passedChecked from public information; code not scanned yet
- UsageiVery new★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs in the cloud
- Transport: Streamable HTTP
- Category: Other
- You need to fill in: Authorization
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 3 of 3 passed
- Runs on the maker’s server: what you ask it goes through their server.
- You fill in yourself:
Authorization
Values marked secret are keys to your account. Never share them or paste them into a chat.
You need
- Runs in the cloudNothing to install; it connects over the internet
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Run this in your terminal
claude mcp add --transport http atoma https://atoma.run/mcp --header 'Authorization: your-value'Fill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"atoma": {
"url": "https://atoma.run/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"atoma": {
"type": "http",
"url": "https://atoma.run/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
Authorizationsecret HTTP headerOptional. "Bearer atoma_…", an API token minted in Settings for one organisation. Without it, the client signs in through OAuth.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
The server asks you to sign in before it lists its tools.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 10, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified against the domain atoma.run (the registry checks domain ownership)
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- ✓ Pass
Endpoint responds
Responded in 719 ms (HTTP 401)
- ✓ Pass
Sign-in with OAuth
Declares OAuth sign-in, so your AI app can take you to log in
- … Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint
Compared with others in Otheri
- More stars than 0% of the other 18955 servers in this category
- Published to the registry more recently than 53% of the other 18955 servers in this category
- Basic checks equal to or better than 100% of the other 18955 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is atoma safe?
It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
You need to fill in: Authorization
Does it run on my computer or in the cloud?
It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 40 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
What can I use instead?
In the same category: Spec Driven Development, Remote Desktop Commander, Backpack
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Remote Desktop Commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Runs in the cloud✓ desktopcommander.app
Backpack
A live record of a business or a life, kept up to date from what people say.
Runs in the cloud✓ andrewslogic.com
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
atoma
Watch a request turn into finished work.
Ask for a drawing, a sound, a report, a data study, a proof or a piece of software. atoma coordinates AI agents to produce it, checks the result against what you asked for, and lets you inspect every step. The bounded decisions along the way, such as which agent takes a task or whether a plan is approved, are taken by Decision, TypeSafe's Jev model.
[Open atoma.run →](https://atoma.run)
Demo · Use cases · A run, step by step · Features · How it works · Self-host · Documentation
[!WARNING]
atoma is under active development. Organisations are designed to be
isolated from one another (projects, workspaces, traces and previews), but
the platform has not been independently audited and we cannot yet guarantee
that isolation, or the absence of other security defects, under every
condition. **Do not include confidential, personal or otherwise sensitive
data in your goals, uploaded files or generated applications**, whether on
atoma.run or on a self-hosted instance exposed to others.
Use the hosted service to evaluate the product, not to process data you could
not afford to see leak. See SECURITY.md to report a vulnerability.
Watch the demo
Explore a two-run business scenario, three-tier coordination, MCP integration and model-cost optimization in this four-minute walkthrough.
https://github.com/user-attachments/assets/3c21219f-9696-4f96-abc8-82944faf5eac
Download the high-quality video (23 MB)
From a business need to a tool you can use
An operations team needs a dashboard. An agency needs a client demo. A product team needs an API prototype. atoma turns those requests into software: web applications, HTTP APIs, command-line tools and their technical documentation.
Start at [atoma.run](https://atoma.run). The web console brings projects, AI execution, acceptance criteria, result previews, model choice and run history into one place. This repository contains the open-source engine and console for teams that want to inspect, extend or self-host them.
What could your team build?
These are example project briefs, not prebuilt industry integrations or customer deployment claims.
| Team or industry | Example project | What it helps you do |
|---|---|---|
| Agencies & consulting | An interactive client demo or project estimator | Turn a proposal into something a client can try |
| Retail & e-commerce | A sales dashboard from exported CSV files | Explore product performance and share a clear view with the team |
| Logistics & operations | An inventory viewer or shipment-status prototype using sample data | Test a workflow before connecting operational systems |
| SaaS & product teams | A web app prototype or HTTP JSON API | Explore a feature and hand working code to engineering |
| Data & engineering teams | A CSV-to-JSON CLI, validation utility or technical documentation | Package a repetitive task into a reusable tool |
For example:
Build a sales dashboard that lets me upload a CSV with date, product, region
and revenue columns. Add filters, monthly totals and a chart by region.
Include sample data and instructions to run it locally.
A run, step by step
- Open Projects → Talk to Atoma, or connect your existing agent through MCP from Settings. Describe the project you want to create or the change you want to make. The integrated assistant uses the saved project brief and latest run to prepare a proposal.
- Review the agent's proposal for a new or existing project, the run goal and optional acceptance criteria. In the integrated assistant, creation and run launch have separate confirmation buttons. Both use the existing MCP. Without criteria, the run drafts its own checklist from the goal.
- Follow the run as agents plan, write files, start servers and check their work, with the model, tokens and cost behind every step.
- Preview web results in a temporary, isolated environment, including a snapshot while the run is still building.
- Keep the deliverable, with its verification record, and publish it to a connected GitHub repository. The project's next run continues from it; a project imported from GitHub starts each run from its default branch.
Previews are review environments; deploying the generated application is a separate step. Project runs use the configured model accounts and consume model quota or incur API charges.
Features
Acceptance criteria you approve before launch
A project run can carry up to twelve criteria, supplied through MCP or the CLI (--criteria ). A line such as GET /api/items is an HTTP check; any other line is judged by review. The host stores the list before planning starts and nothing can change it afterwards. The planner is told the user wrote it, and the final check is told which HTTP checks were actually observed on servers the run itself started.
Without a list, the run drafts one from the goal with a single call to the cheapest model. A drafted list can only add to what the final check looks for; it can never make a run pass.
Finished work is never thrown away
A run that exhausts its budget after completing at least one phase, or whose result the final check refuses after one remediation attempt, is kept as incomplete rather than discarded — including when the deadline falls while its finished work is still being checked. Its finished phases stay in the workspace, and the console explains in plain language why it stopped and what to do next, with the technical reasons one click away. In a project created in atoma, the next run starts from that workspace and is told why the previous one stopped; a project imported from GitHub starts each run from its default branch, so unpublished changes are not carried over.
Choose the models, then compare them
Each tier (workers, supervisors, planners) takes its own model selector, of the form : : , across Anthropic, OpenAI, Google DeepMind, xAI, Meta, Mistral, Alibaba Qwen, DeepSeek, Moonshot, Z.ai and a self-hosted Ollama: api: bills a key, sub: the host's Claude or ChatGPT subscription, and own: the member's own ChatGPT account, whose available models Settings lists, or their own Claude Code token (beta). The operator can delegate the host subscription to named members without making them administrators. The offered models and their per-token prices are one versioned file, src/core/modelCatalog.json, kept current with npm run models -- refresh: prices are a dated history, so a vendor's change applies from its day without re-pricing what came before.
Every run records the models it was pinned to and the models the provider actually served. A delivered or incomplete run of a project created in atoma can be rerun on other models through the API or MCP: same goal, same acceptance criteria, same starting workspace. The rerun sits beside the project's history, so you can compare cost, time and result; it never publishes and never seeds a later run.
Start from your repository, publish back to it
Install the GitHub App to import a repository into a project or create a new one, then publish delivered results to it. A project's earlier deliverables, including Markdown, CSV, PDF and Office documents, are indexed so later runs can search them and cite exact passages. TypeScript/JavaScript sources also expose symbols and resolved local imports through Haystack; clients use atoma_run_search to inspect saved versions (scope and evaluation). See GitHub App setup.
Verification you can read
Supervisors check results with evidence the run produced: files, command exit codes, HTTP probes, and browser checks laid out at the viewport widths the goal asks for. They never replay shell commands a model wrote. Every verdict, retry and refusal is in the timeline, and the console is available in thirteen languages, on desktop and mobile.
For an existing static page, the host also replays browser checks recorded by earlier runs before editing begins and again before delivery. Regressions go to the final review, which distinguishes requested changes from lost behaviour. Read-only phases restore the files they changed, and workers read existing files before editing them. See inherited browser checks.
Decision handles bounded decisions
Decision, TypeSafe's Jev model, helps choose an agent or recipe, approve intermediate plans and results when the evidence permits it, and avoid learning equivalent recipes twice. It answers structured questions; the run's models still plan, write code and explain refusals. Uncertain or unavailable answers go back to those models, and final delivery acceptance stays independent of Decision.
The initial goal and starting repository guide the root tissue choice. Decision selects a suitable registered L3; if the choice is uncertain or no tissue fits, a bounded model call reuses one or requests a new capability. New tissue prompts are authored by the platform's pinned L3 model using the platform's credentials. The legacy run:build command also accepts analysis and explanation tasks.
Decision is enabled for every organisation when the host has TYPESAFE_API_KEY, unless the operator sets ATOMA_JEV=0. Decision context, including task text and evidence excerpts, is sent to TypeSafe as described in the service terms. The timeline records its decisions, latency and separate cost; a sample of approvals is reviewed by the model for comparison. See the architecture guide.
Adjust run limits without redeploying
Platform administrators can change run budgets, token and spend ceilings, call timeouts and tool-iteration limits in Settings or with npm run settings. Changes are recorded in the audit journal and apply to new runs. A token or spend ceiling stops further model calls and preserves completed phases as an incomplete run. See operating run limits.
Connect an existing agent through MCP
The console serves an HTTP MCP endpoint at https:// /mcp. Compatible clients such as Claude Code or Codex can create projects, draft run goals from the conversation and repository context, start runs, pass acceptance criteria, request reruns and read traces, costs and diagnostics. The Web console also provides an integrated assistant for customers without an external agent, and remains the place to review projects and results. Choose a personal subscription, organisation API key or configured platform API connection; conversation usage is accounted separately from runs; see setup and limits. Conversations can continue in either direction between Atoma and an MCP client.
Only the beginning is shown; read the rest in the repo