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Neo — AI/ML Engineering

Neo — AI/ML Engineering

Local
heyneo.comDeveloper Tools2Updated 6w ago

About Neo — AI/ML Engineering

neo-mcp connects Neo, an autonomous AI/ML engineering service, to MCP-enabled coding tools so data scientists, ML engineers, and researchers can delegate workflows from their editor. It tracks tasks and writes resulting artifacts to the local repository.

Key features

  1. 01Submit and manage AI/ML tasks
  2. 02Track status and retrieve outputs
  3. 03Pause, resume, and stop tasks
  4. 04Register and test integrations
  5. 05Verify local artifacts and evidence

Use cases

  1. 01Delegate model training and evaluation
  2. 02Build RAG or semantic search workflows
  3. 03Run data analysis and forecasting
  4. 04Generate vision or speech workflows

MCP support

neo-mcp is an MCP server that exposes task submission, status, output, feedback, control, integration, and verification tools to coding clients. It is configured with the Neo API key and communicates with Neo while outputs are written locally.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"neoairesearch-neo": {
"command": "uvx",
"args": [
"neo-mcp"
],
"env": {
"NEO_SECRET_KEY": "<NEO_SECRET_KEY>",
"NEO_ENVIRONMENT": "<NEO_ENVIRONMENT>",
"NEO_WORKSPACE_DIR": "<NEO_WORKSPACE_DIR>"
}
}
}
}
Run commanduvx neo-mcp
Environment variables to set
NEO_SECRET_KEYSecretYour Neo API secret key (starts with sk-v1-). Get one at https://heyneo.com.
NEO_ENVIRONMENTNeo backend environment: 'production' (default) or 'staging'.
NEO_WORKSPACE_DIRProject root where Neo tasks read and write files. Must be the git/project root.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypineo-mcp0.5.5

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Neo MCP — AI engineering, without leaving your editor

PyPI Python License: MIT Downloads

Stay in Claude Code, Cursor, or Codex and hand your AI/ML work to Neo — an autonomous AI engineer that plans, builds, runs, and evaluates entire workflows from a plain-English request. neo-mcp is the Model Context Protocol server that connects Neo to the AI coding tools you already use, so you never switch tabs: ask your agent to send a task to Neo, and a local daemon writes the resulting code, models, metrics, and reports straight into your repo — on your machine, nothing stored remotely.

Because Neo is purpose-optimized for AI engineering — not a general-purpose coding assistant — it goes deeper on ML, LLM, and data workflows than a general coding agent can.

📚 Docs: https://docs.heyneo.com/neo-mcp · 🔑 Get an API key: Neo dashboard

What MCP unlocks

  • 🧩 Stay in your editor — drive Neo from Claude Code, Cursor, VS Code (Copilot), Windsurf, Zed, Continue, or Codex. No new app, no context switching.
  • 🔬 More depth — autonomous planning, experiments, evaluation, and iteration tuned for AI/ML, beyond what a generic coding agent attempts.
  • 💾 Local-first — every output file is written to your machine; nothing is stored remotely.

What you can build with Neo

  • 🤖 Generative AI & LLMs — RAG & semantic search, agents & chatbots, fine-tuning (Llama, Qwen, Gemma), document analysis
  • 🧠 ML & deep learning — PyTorch / TensorFlow / scikit-learn training, architecture search, evaluation
  • 📊 Data science & analytics — EDA, feature engineering, forecasting, segmentation, A/B testing, reporting
  • 👁️ Computer vision — image classification, object detection, OCR
  • 🎤 Speech & audio — speech-to-text, text-to-speech, audio classification
  • 🔌 Bring your own keys — GitHub, HuggingFace, Anthropic, OpenRouter, OpenAI, AWS S3, Weights & Biases, Kaggle — stored locally, injected as env vars

For data scientists, ML & LLM engineers, analysts, researchers, and PMs who want results, not boilerplate.


Install

pip install neo-mcp

Requires Python 3.11+ on Linux, macOS, or Windows through WSL. Native Windows cmd.exe/PowerShell execution is intentionally unsupported because Neo commands use POSIX shell and container-path semantics.

Tip: use pipx install neo-mcp to install in an isolated environment and avoid conflicts with your project's virtualenv.


Use Neo from your editor

Neo runs in every major MCP-enabled AI editor — set it up once below. Replace sk-v1-YOUR_KEY with your actual key.


Claude Code
claude mcp add --scope user neo \
  -e NEO_SECRET_KEY=sk-v1-YOUR_KEY \
  -- neo-mcp

Open a new Claude Code session after running this. Neo tools load at session start.

Scope options: --scope user (global, recommended) · --scope project (writes .mcp.json in current repo) · --scope local (this machine only)

Verify it registered:

claude mcp list

Cursor

~/.cursor/mcp.json:

{
  "mcpServers": {
    "neo": {
      "command": "neo-mcp",
      "env": {
        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"
      }
    }
  }
}

Restart Cursor after editing.


Windsurf

~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "neo": {
      "command": "neo-mcp",
      "env": {
        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"
      }
    }
  }
}

Restart Windsurf after editing.


VS Code (GitHub Copilot)

.vscode/mcp.json in your workspace root (requires VS Code 1.99+):

{
  "servers": {
    "neo": {
      "type": "stdio",
      "command": "neo-mcp",
      "env": {
        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"
      }
    }
  }
}

Zed

~/.config/zed/settings.json:

{
  "context_servers": {
    "neo": {
      "source": "custom",
      "command": {
        "path": "neo-mcp",
        "args": [],
        "env": {
          "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"
        }
      }
    }
  }
}

Continue.dev

~/.continue/config.json:

{
  "mcpServers": [
    {
      "name": "neo",
      "transport": {
        "type": "stdio",
        "command": "neo-mcp",
        "env": {
          "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"
        }
      }
    }
  ]
}

OpenAI Codex CLI

~/.codex/config.json:

{
  "mcpServers": {
    "neo": {
      "command": "neo-mcp",
      "env": {
        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"
      }
    }
  }
}

Tools

ToolDescription
neo_submit_taskSubmit a locally destructive AI/ML task; one active owner per workspace.
neo_list_tasksList running and recent tasks — reconnects pollers automatically.
neo_task_statusCompact live backend-reported status; telemetry is not independent proof.
neo_get_messagesRead full task output when COMPLETED. Capped at ~20 000 tokens.
neo_send_feedbackReply when Neo asks a question (WAITING_FOR_FEEDBACK).
neo_pause_taskPause a running task.
neo_resume_taskResume a paused task.
neo_stop_taskStop and clean up a task permanently.
neo_list_integrationsList stored third-party API keys (names only — never the value).
neo_add_integrationRegister a GitHub PAT / HuggingFace token / Anthropic key / OpenRouter key so Neo tasks can use it as an env var.
neo_test_integrationCall the provider's API to confirm a stored key is still valid.
neo_remove_integrationDelete a stored key from this machine.
neo_get_execution_evidenceRead bounded, hash-chained local execution observations.
neo_verify_taskVerify local artifacts with explicit file assertions and acceptance commands.

Integration tools store credentials locally (file 0o600 under ~/.neo/integrations/, or OS keyring with NEO_INTEGRATIONS_BACKEND=keyring). Keys never leave your machine. See the full guide at docs/INTEGRATIONS.md.


Environment variables

VariableRequiredDescription
NEO_SECRET_KEYYesYour Neo API key (sk-v1-...) from the Neo dashboard
NEO_DEPLOYMENT_IDNoPin a specific deployment UUID (auto-generated and persisted by default)
NEO_WORKSPACE_DIRNoOverride working directory (useful in Docker)
NEO_READ_ONLYNotrue — expose only status, messages, task/evidence reads, integration-test/list, and BYOK-list tools; reject every mutation and verification command
NEO_INIT_CHAT_TIMEOUT_SECONDSNoSubmit timeout in seconds; defaults to 60 to match the production VS Code client
NEO_POLLER_READY_TIMEOUT_SECONDSNoMaximum pre-submit wait for the local daemon to establish its backend poll connection; defaults to 15

Troubleshooting

SymptomFix
neo-mcp: command not foundRe-run pip install neo-mcp and verify your PATH with which neo-mcp
Tools don't appear after registeringOpen a new session — MCP tools load at session start, not mid-session
Invalid API key (401)Re-check your key in the Neo dashboard
Trial or quota ended (403)Top up at the Neo dashboard
Task submitted but no files writtenDaemon failed to start — run neo-mcp doctor to diagnose
Status stuck on RUNNINGCall neo_task_status to check; run neo-mcp status to inspect the daemon

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.