Add Atomic Chat MCP server so the container agent can call local models served by the Atomic Chat desktop app via its OpenAI-compatible API.
name: add-atomic-chat-tool
description: Add Atomic Chat MCP server so the container agent can call local models served by the Atomic Chat desktop app via its OpenAI-compatible API.
Add Atomic Chat Integration
This skill adds a stdio-based MCP server that exposes models running in the local Atomic Chat desktop app as tools for the container agent. Claude remains the orchestrator but can offload work to local models served by Atomic Chat on http://127.0.0.1:1337/v1 (OpenAI-compatible).
Tools exposed:
atomic_chat_list_models — list models currently available in Atomic Chat (GET /v1/models)
atomic_chat_generate — send a prompt to a specified model and return the response (POST /v1/chat/completions)
Model management (download, delete) is done through the Atomic Chat desktop UI — the app is a fork of Jan and manages its own model library.
The skill ships the MCP server source (and its test) in this folder and copies them into the agent-runner tree at install time, then registers the server in index.ts and forwards host env vars in container-runner.ts. Registering the server is enough to expose its tools — the agent's allow-pattern (mcp__atomic_chat__*) is derived from the registered server name.
Phase 1: Pre-flight
Check if already applied
Check if exists. If it does, skip to Phase 3 (Configure).
Verify Atomic Chat is installed and its local API server is running. On the host:
curl -s http://127.0.0.1:1337/v1/models | head
If the request fails:
Install Atomic Chat from the latest release (macOS only for now — atomic-chat.dmg).
Open the app.
Open Settings → Local API Server and make sure it's enabled on port 1337.
Go to the Hub (or Models) tab and download at least one model (e.g. Llama 3.2 3B, Qwen 2.5 Coder 7B).
Load the model once by sending any message in Atomic Chat's UI to warm it up.
Phase 2: Apply Code Changes
Copy the skill's source and tests into both trees
This skill reaches into both the container (Bun) tree and the host (Node) tree, so its
files go into both, alongside the integration points they cover.
S=.claude/skills/add-atomic-chat-tool
# Container (Bun) tree — the MCP server and the registration wiring test
cp $S/atomic-chat-mcp-stdio.ts container/agent-runner/src/atomic-chat-mcp-stdio.ts
cp $S/atomic-chat-registration.test.ts container/agent-runner/src/atomic-chat-registration.test.ts
# Host (Node) tree — the env-forwarding helper and the wiring test
cp $S/atomic-chat-env.ts src/atomic-chat-env.ts
cp $S/atomic-chat-wiring.test.ts src/atomic-chat-wiring.test.ts
Register the MCP server in the agent-runner
Edit container/agent-runner/src/index.ts. Find the mcpServers object that currently looks like this:
atomic-chat-registration.test.ts asserts this entry is present and points at the server module — the tool only appears to the agent if it is registered here.
Forward host env vars into the container
The env-forwarding logic lives in the copied src/atomic-chat-env.ts (atomicChatEnv()), so the reach-in into composeSessionSpec is a single spread.
Import it in src/container-runner.ts (alongside the other local imports):
import { atomicChatEnv } from './atomic-chat-env.js';
Then, in composeSessionSpec, find the contributedEnv literal and spread the helper at the end. The contributed lane — not the composed env literal — because ATOMIC_CHAT_API_KEY is credential-NAMED and the composed lane's key-name check would refuse the spawn; the contributed lane exempts the name and still refuses credential-shaped values:
atomic-chat-wiring.test.ts asserts this ...atomicChatEnv() spread exists inside composeSessionSpec.
Surface [ATOMIC] log lines at info level
Shared block. This rewrites the driver's container-stderr logger, which other local-model tools (e.g. add-ollama-tool for [OLLAMA]) also edit to surface their own prefix. Touch only the [ATOMIC] branch and leave the rest of the block intact, so the edits coexist and removal restores it cleanly.
Container stderr now lands in the Docker driver: in src/drivers/docker-driver.ts, inside DockerHandle.start(), find the stderr handler:
# Atomic Chat MCP tool (.claude/skills/add-atomic-chat-tool)
# Override the host where Atomic Chat exposes its OpenAI-compatible API.
# Default: http://host.docker.internal:1337 (with fallback to localhost)
# ATOMIC_CHAT_HOST=http://host.docker.internal:1337
# Optional API key. Leave unset for a local Atomic Chat install — it does not require auth.
# ATOMIC_CHAT_API_KEY=
Validate code changes
pnpm run build
pnpm exec tsc -p container/agent-runner/tsconfig.json --noEmit
# Host tree: composeSessionSpec wiring
pnpm exec vitest run src/atomic-chat-wiring.test.ts
# Container tree: index.ts registration
(cd container/agent-runner && bun test src/atomic-chat-registration.test.ts)
./container/build.sh
All must be clean before proceeding. The wiring and registration tests confirm the two
integration points — the composeSessionSpec spread and the index.ts registration — are
actually in place; a failure means one drifted. (The MCP server's own request/response
behavior against Atomic Chat is the author's build-time concern, not part of these tests —
verify it manually in Phase 4.)
Phase 3: Configure
Set Atomic Chat host (optional)
By default, the MCP server connects to http://host.docker.internal:1337 (Docker Desktop) with a fallback to localhost. To use a custom host, add to .env:
Atomic Chat does not require authentication when running locally — leave this unset. Only set it if you've put Atomic Chat behind a reverse proxy that enforces auth:
Send a message like: "use atomic chat to tell me the capital of France"
The agent should use atomic_chat_list_models to find available models, then atomic_chat_generate to get a response.
Check logs if needed
tail -f logs/nanoclaw.log | grep -i atomic
Look for:
[ATOMIC] Listing models... — list request started
[ATOMIC] Found N models — models discovered
[ATOMIC] >>> Generating with <model> — generation started
[ATOMIC] <<< Done: <model> | Xs | N tokens | M chars — generation completed
Troubleshooting
Agent says "Atomic Chat is not installed" or tries to run a CLI
The agent is looking for a CLI that doesn't exist instead of using the MCP tools. This means:
The MCP server wasn't copied — check container/agent-runner/src/atomic-chat-mcp-stdio.ts exists
The MCP server wasn't registered — check container/agent-runner/src/index.ts has the atomic_chat entry in mcpServers (the allow-pattern is derived from this, so registration is the only thing to check)
The container wasn't rebuilt — run ./container/build.sh
"Failed to connect to Atomic Chat"
Verify the host API is reachable: curl http://127.0.0.1:1337/v1/models
Confirm the Local API Server is enabled in Atomic Chat's settings
Check Docker can reach the host: docker run --rm curlimages/curl curl -s http://host.docker.internal:1337/v1/models
If using a custom host, check ATOMIC_CHAT_HOST in .env
model not found / 404 on generate
The model ID passed to atomic_chat_generate must exactly match one of the IDs returned by atomic_chat_list_models. Ask the agent to list models first, then pick one from that list.
Slow first response
Atomic Chat lazy-loads models into memory on first use. The initial call may take longer while the model warms up. Subsequent calls against the same model are fast.
Agent doesn't use Atomic Chat tools
The agent may not know about the tools. Try being explicit: "use the atomic_chat_generate tool with llama3.2-3b-instruct to answer: ..."
Context window or output size issues
Atomic Chat respects each model's native context length. If you hit limits, pass max_tokens explicitly when calling atomic_chat_generate, or switch to a model with a larger context window in the Atomic Chat UI.
How do I install add-atomic-chat-tool?
Download .claude/skills/add-atomic-chat-tool/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does add-atomic-chat-tool live?
The publisher keeps it at https://github.com/nanocoai/nanoclaw.
Which agents can use add-atomic-chat-tool?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.