Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
MCP server for structured reasoning and graph review
Atom of Thoughts adds three tools to an MCP client: fast reasoning, deeper decomposition, and session and approval management. Each chain is split into atoms with types, dependencies, and confidence scores, then can be viewed in a browser graph or a terminal TUI.
Builders who want their agent's reasoning to be structured, reviewable, and split into tracked steps.
You can inspect and approve an agent's reasoning instead of treating the answer as a black box.
What it does
Atomic reasoning chain
Breaks work into premise, reasoning, hypothesis, verification, and conclusion atoms with dependency links and confidence scores.
Fast and full tools
Provides `AoT-fast` for shorter decisions and `AoT-full` for deeper decomposition, plus `atomcommands` for session and approval control.
Browser visualization
Opens a D3-based graph when `viz: true` is passed, with colored atoms, confidence rings, and approve or reject actions.
Live TUI
Shows atoms streaming in a second terminal pane, with keys for accept, reject, star, pause, and submit.
Session isolation
Keeps reasoning chains in separate sessions so unrelated threads do not bleed into each other.
Approval feedback loop
Lets the user approve or reject atoms, then feeds that verdict back through the server's approval flow.
How to get it
- 1npm global
npm install -g @dioptx/mcp-atom-of-thoughts
- 2Smithery
npx -y @smithery/cli install @dioptx/mcp-atom-of-thoughts --client claude
- 3Docker
docker build -t aot .
README
Quickstart
1. Add to your MCP config:
{
"mcpServers": {
"atom-of-thoughts": {
"command": "npx",
"args": ["-y", "@dioptx/mcp-atom-of-thoughts"]
}
}
}
2. Restart your client.
3. Ask the model to reason something through:
"Use AoT-fast to think through whether we should use JWT or session-based auth for the API."
The model breaks the problem into five kinds of atoms (premise, reasoning, hypothesis, verification, conclusion), each tagged with a confidence score. You get a structured chain you can audit, not a black-box answer.
[!TIP] Works with Claude Code, Cursor, Windsurf, or any MCP-aware client.
Install
npx (recommended; zero install, always latest)
{ "command": "npx", "args": ["-y", "@dioptx/mcp-atom-of-thoughts"] }
npm global
npm install -g @dioptx/mcp-atom-of-thoughts
{ "command": "mcp-atom-of-thoughts" }
Smithery
npx -y @smithery/cli install @dioptx/mcp-atom-of-thoughts --client claude
Docker
docker build -t aot .
{ "command": "docker", "args": ["run", "-i", "--rm", "aot"] }
How it works
graph LR
P["P · Premise"]:::premise --> R["R · Reasoning"]:::reasoning
R --> H["H · Hypothesis"]:::hypothesis
H --> V["V · Verification"]:::verification
V --> C["C · Conclusion"]:::conclusion
classDef premise fill:#6b7280,stroke:#9ca3af,color:#fff,font-weight:bold
classDef reasoning fill:#3b82f6,stroke:#60a5fa,color:#fff,font-weight:bold
classDef hypothesis fill:#eab308,stroke:#facc15,color:#000,font-weight:bold
classDef verification fill:#06b6d4,stroke:#22d3ee,color:#fff,font-weight:bold
classDef conclusion fill:#22c55e,stroke:#4ade80,color:#fff,font-weight:bold
Atoms chain through dependencies. Each carries a confidence score from 0 to 1. Reasoning terminates when a high-confidence conclusion lands or max depth is hit. Each problem runs in its own session, so two threads of thought never bleed into each other.
Tools
Three tools cover the full surface:
| Tool | When to reach for it |
|---|---|
AoT-fast | Default. Tradeoffs, debugging, decisions, option evaluation. Depth 3. |
AoT-full | Plans, architecture, decomposition into sub-problems. Depth 5. |
atomcommands | Sessions, export, approval polling, decomposition lifecycle. |
Quick example
AoT-fast({atomId:"P1", content:"API returns 500 on POST /users", atomType:"premise"})
AoT-fast({atomId:"R1", content:"Unhandled exception in route handler", atomType:"reasoning", dependencies:["P1"]})
AoT-fast({atomId:"C1", content:"Add try-catch in POST handler", atomType:"conclusion", dependencies:["R1"], confidence:0.9})
Only atomId, content, and atomType are required. Everything else has sensible defaults.
Visualization
Pass viz: true on any call to open an interactive D3 graph in the browser:
AoT-fast({atomId:"C1", ..., viz: true})
Approve and reject decisions POST back to the server over HTTP. No filesystem polling.
Live TUI
Watch the model reason in a second terminal pane while it works, and feed approve/reject decisions back into the next tool call. The event feed is on by default; nothing extra to configure.
In a second pane next to your LLM client:
npx -y @dioptx/mcp-atom-of-thoughts tui
1. Watch reasoning unfold

Atoms appear as the model emits them, walking the chain premise → reasoning → hypothesis → verification → conclusion. Confidence bars fill in real time, dependencies show as inline arrows, and a velocity sparkline tracks event rate. Auto-scroll keeps the newest atom selected.
2. Give granular feedback

j / k move the selection. a accepts an atom; * stars it as critical context; r rejects it and prompts for a one-line reason. s submits the verdict. The submit flash tells you exactly what to do next: ask the model to call atomcommands check_approval. The verdict is written as the same approval JSON the existing file-fallback path already polls for, so feedback flows back through a contract the server already understands. Zero new wire protocol.
3. Customize the view

t opens settings: confidence threshold to hide low-confidence atoms, color theme (vibrant, soft, or mono), compact mode, dependency arrows toggle. ? shows the full keymap.
Keys reference
| Key | Action |
|---|---|
j / k | Move selection |
a | Accept the selected atom |
r | Reject (prompts for a one-line reason) |
u | Clear feedback on the selected atom |
* | Star as critical context |
s | Submit verdict (writes aot-approval-*.json) |
t | Settings (threshold, theme, compact mode, deps) |
? | Keys help |
space | Pause / resume event stream |
q | Quit |
[!TIP] Skip setup and see it in action:
npx -y @dioptx/mcp-atom-of-thoughts tui --demo
Configuration
{
"args": ["-y", "@dioptx/mcp-atom-of-thoughts", "--mode", "fast", "--viz", "never"]
}
| Flag | Default | Effect |
|---|---|---|
--mode full|fast|both | both | Which tools to register |
--viz auto|always|never | auto | auto: render on viz:true. always: render every call. never: skip (CI) |
--max-depth <n> | 5 / 3 | Override depth limit |
--output-dir <path> | OS temp | Where to write viz HTML |
--downloads-dir <path> | ~/Downloads | Approval JSON fallback |
Sessions
Each reasoning chain gets its own session. Default ID: "default".
atomcommands new_sessioncreates and activates a new one.atomcommands switch_session/list_sessions/reset_sessionfor management.- When reasoning terminates, the session auto-archives. The next zero-dependency atom auto-spawns
default-2,default-3, and so on. - Or pass
sessionIdon any AoT call to target one explicitly.
Two problems in one MCP process stay isolated without manual session management.
Browser visualization (alternative to the TUI)
Prefer a browser tab to a terminal pane? Pass viz: true on any AoT call. The server writes a self-contained HTML file (D3 inlined, works offline) and opens it:
- Force-directed graph colored by atom type with confidence rings
- Sidebar to approve or reject phases or individual atoms
- Approve / reject POSTs to a local
127.0.0.1listener on an ephemeral port; falls back to a~/Downloadsfile scan if the listener can't bind
The TUI and the browser viz both feed atomcommands check_approval. Pick whichever fits your workflow.
Install methods
npx (zero install):
{ "command": "npx", "args": ["-y", "@dioptx/mcp-atom-of-thoughts"] }
npm global:
npm install -g @dioptx/mcp-atom-of-thoughts
Smithery:
npx -y @smithery/cli install @dioptx/mcp-atom-of-thoughts --client claude
Docker:
docker build -t aot . && docker run -i --rm aot
Development
git clone https://github.com/dioptx/mcp-atom-of-thoughts.git
cd mcp-atom-of-thoughts
npm install
npm test # 183 tests (unit + e2e)
npm run build
Migrating from v2
See MIGRATION_v2_to_v3.md for the full lookup table. The short version:
AoT-lightis nowAoT-fastAoTis nowAoT-fullgenerate_visualizationis nowviz: trueon any AoT callexport_graphandcheck_approvalare nowatomcommandssubcommands--no-vizand--no-approvalare replaced by--viz auto|always|never
MIT. Based on Atom of Thoughts.
Files in the repo
- .github
- assets
- src
- tests
- .gitignore
- .npmignore
- AUDIT_v2_to_v3_dotfiles.md
- CHANGELOG.md
- CLAUDE.md
- CONTRIBUTING.md
- Dockerfile
- LICENSE
- MIGRATION_v2_to_v3.md
- package-lock.json
- package.json
- PLAN_V3.md
- README.agents.md
- README.md
- SECURITY.md
- smithery.yaml
- tsconfig.json
- vitest.config.ts
Discussion (0)
Ask about usage, or say what you built with itSign in to join the discussion.
No comments yet. Be the first to say what this is good for.
More connectors
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

Universal provider proxy for OpenAI Codex & Claude Code — use any LLM (Claude, Gemini, Grok, DeepSeek, Ollama…) with Codex CLI, App, SDK, and Claude Code
Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.
Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
Stop your AI from making things up — it proposes, deterministic tools decide, every claim checked against ground truth with evidence. Grounded facts and context survive resets. Reverse engineering is the proving ground. MCP server + CLI.