Naja Scope
🐍 🏠 - Lets AI agents explore elaborated SystemVerilog/Verilog designs (RTL or gate-level netlists) without pasting RTL into context: trace signal fan-in/fan-out, walk module hierarchy, and jump to exact file:line source, backed by the najaeda netlist engine.
Quick Install
{
"mcpServers": {
"najaeda-naja-scope": {
"command": "npx",
"args": [
"-y",
"najaeda-naja-scope"
]
}
}
}Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.
Documentation Overview
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:lineranges, 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.
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.
| Approach | Correct answers | Conversation turns | Input tokens |
|---|---|---|---|
| naja-scope | 17 / 17 | 77 | 182 k |
| grep + read source | 10 / 17 | 123 | 888 k |
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.svwith topuart_top, then show me everything that drivestx_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 netlistbuild/top.v, and tell me what cellstopis built from and what drivesdata_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/Zvalues. - 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
najaedaruns (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.