naja-scope

Let your AI assistant explore SystemVerilog designs — without pasting source code into the chat.

naja-scope is an MCP server that gives AI agents (Claude, and any MCP-compatible assistant) a precise, structured view of your elaborated SystemVerilog design. Instead of dumping thousands of lines of RTL into the model's context, the agent asks targeted questions — what drives this signal? what's inside this module? where does this net come from? — and gets back small, exact answers with file-and-line references.

Built on the najaeda netlist engine.

Why

Large designs don't fit in a chat window. Pasting RTL is slow, expensive, and the model still can't reliably trace connectivity across hierarchy. naja-scope turns your design into something an agent can navigate:

• 🔎 Trace connectivity — find what drives or loads any signal, across module boundaries.

• 🌲 Walk the hierarchy — explore modules, instances, and ports on demand.

• 🎯 Jump to source — every answer comes with file:line ranges, so the agent can quote the exact RTL that matters.

• 🧩 Logic cones — trace fan-in / fan-out combinational cones up to the register boundary.

• 💡 Recover design intent — enum state names, struct/union fields, and parameter formulas that normally vanish when a design is elaborated.

Works on RTL and gate-level netlists alike: load elaborated SystemVerilog, or load a post-synthesis structural Verilog netlist together with its Liberty standard-cell library and navigate the gates the same way (see Gate-level designs).

All responses are token-bounded: lists paginate, large results truncate with clear markers. Your context stays small; your answers stay accurate.

Related MCP server: EDA Tools MCP Server

Does it actually help?

We ran a head-to-head on CVA6 (a production RISC-V core): the same 17 design questions, answered by Claude once with only naja-scope and once with only grep/file reading over the source tree.

More correct answers, fewer back-and-forth turns, and ~5× fewer tokens — the agent stops scrolling through files and goes straight to the structural answer.

Install

pip install naja-scope        # pulls najaeda and the MCP runtime from PyPI
naja-scope-mcp                # stdio MCP server

Connect it to Claude Code

claude mcp add naja-scope -- naja-scope-mcp

Or add it to any MCP client's config:

{
  "mcpServers": {
    "naja-scope": {
      "command": "naja-scope-mcp"
    }
  }
}

Then just ask your assistant to load a design and start exploring:

"Load my UART design from rtl/uart.sv with top uart_top, then show me everything that drives tx_o."

The agent loads the design once and answers follow-up questions instantly — no re-reading source, no giant pastes.

Connect it to ChatGPT

ChatGPT connects to MCP servers over an HTTP endpoint (custom connectors / Developer mode), so run naja-scope as an HTTP server instead of stdio:

naja-scope-mcp --transport streamable-http --host 127.0.0.1 --port 8000

This serves MCP at http://<host>:8000/mcp. Because ChatGPT reaches the server over the network, expose that URL where ChatGPT can see it — e.g. a public tunnel for a local run:

# example: a tunnel to your local server (ngrok, cloudflared, …)
ngrok http 8000        # -> https://<something>.ngrok.app  →  add /mcp

Then in ChatGPT, open Settings → Connectors (enable Developer mode if needed), add a custom connector, and paste the server URL (https://<your-host>/mcp). Once connected, ask it to load a design and explore exactly as above. (ChatGPT's connector UI evolves; the constant is: it needs an HTTPS MCP URL, which --transport streamable-http provides.)

⚠️ The HTTP server has no built-in auth — only expose it over a trusted tunnel, and prefer short-lived tunnels for local experiments.

Gate-level designs

Already synthesized? Load the structural Verilog netlist together with the Liberty library that defines its standard cells, and navigate the gates the same way as RTL:

"Load the Liberty library pdk/stdcells.lib, then the gate netlist build/top.v, and tell me what cells top is built from and what drives data_out."

Hierarchy, per-cell counts (get_module_card), drivers/loads, and logic cones all work on the netlist; cones stop at the sequential cells. A gate netlist carries no source line info, so get_source applies to RTL only. A runnable example lives in examples/ (stdcells.lib + counter2.v + gate_level.py).

What you can ask

Once a design is loaded, your assistant can:

• Resolve any signal or instance by hierarchical path (with glob and did-you-mean suggestions).

• Find objects design-wide by pattern.

• Show the hierarchy of any module.

• Get drivers / loads of a net — the real endpoints, across hierarchy; literal drivers preserve four-state 0 / 1 / X / Z values.

• Trace logic cones (fan-in / fan-out) and see the register frontier.

• Get source — the exact SystemVerilog lines behind any object.

• Get a module card — ports, counts, clock/reset at a glance.

• Recover design intent — state-machine names, struct fields, parameter expressions lost during elaboration.

A runnable end-to-end walkthrough lives in examples/, including a version that runs against CVA6 (a production RISC-V core, cloned on demand — see examples/cva6_demo.sh).

The Python escape hatch (off by default)

naja-scope also has a query_python tool that runs Python directly against the loaded design, for queries the typed tools above cannot express. It is not registered unless you opt in:

NAJA_SCOPE_ENABLE_PYTHON=1 naja-scope-mcp

It is unsandboxed eval/exec inside the server process — read-only by convention, not enforced — so anything that can reach the server can run arbitrary Python as the server's user. That matters most under --transport streamable-http, where the server listens on a socket. Leave it off unless you need it and trust every client that can reach the endpoint.

Requirements

• Python 3.10+

• Works anywhere najaeda runs (Linux, macOS, Windows)

Development

# from a checkout
python3 -m venv .venv
.venv/bin/pip install -e .
.venv/bin/python -m pytest -q

The full test suite runs against a plain pip install of najaeda — no native build required. The CVA6 cross-hierarchy cone regression (tests/test_zzz_cone_cva6.py) is slow and skips automatically unless a CVA6 snapshot is present.

Support & contact

• 🐛 Found a bug or have a feature request? Open an issue on GitHub →

• 📫 Get in touch: contact@keplertech.io

License

Apache-2.0. See LICENSE.