TraceWeave

Name: TraceWeave
Author: gokeshenzhen

by gokeshenzhenversion 2.0.2i★ –iRuns on your computer

Registry nameio.github.gokeshenzhen/traceweave

Install

Overview

Description from the maker (original English)

Portable MCP runtime for RTL logs and VCD; repository setup adds FSDB and site EDA integration

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "traceweave": {
      "command": "uvx",
      "args": [
        "traceweave-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add traceweave -- uvx traceweave-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "traceweave": {
      "command": "uvx",
      "args": [
        "traceweave-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "traceweave": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "traceweave-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account gokeshenzhen, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 0% of the other 4906 servers in this category
Published to the registry more recently than 38% of the other 4906 servers in this category
Basic checks equal to or better than 100% of the other 4906 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is TraceWeave safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.0.2) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

🐙 TraceWeave

English · 简体中文

An MCP server for evidence-driven simulation debugging

TraceWeave is an MCP server for RTL / SoC debugging. It connects compile records, simulation logs, VCD/FSDB waveforms, and RTL source to your AI assistant, helping you work from a failed simulation to the relevant time, signals, and driving logic.

Use it with Claude Code, Codex, Copilot, or another MCP client. Describe the problem in natural language; the assistant uses tools to investigate, trace, and verify, with evidence you can review.

Use Cases · Installation · Client Setup · Start Debugging · Tool Quick Reference · FAQ · Documentation and Feedback

Use Cases

Problem	How TraceWeave helps
A simulation times out or hangs, with no clear starting point	Summarize failures and scan interface handshakes to narrow down the affected interfaces and time window
A scoreboard mismatch or different results between runs	Compare failure records and waveforms, locate observed differences, and trace data and control sources on both sides
A signal becomes X/Z	Follow drivers and review waveform history to check whether a register sampled and retained an unknown value
Packed buses are hard to read, or TL-UL requests are not completing	Inspect address, data, and control fields individually, then check request/response handshakes, latency, and completion
Suspected ties, unconnected inputs, or magic-word conditions	Statically scan source code for constant connections, open inputs, and constant comparisons as investigation leads; no simulation run or waveform required
A deep SoC hierarchy with many modules and interfaces	Browse hierarchy on demand, locate instances and source files, and follow drivers, consumers, and connectivity paths
A debugging hypothesis needs evidence	Sample by cycle and check timing conditions, handshake stability, and transaction completion

Supports VCS / Xcelium / Verilator simulation logs and VCD / FSDB / FST waveforms. Exported formal waveforms can also be queried; automatic artifact discovery currently supports JasperGold.

Optional FST support on Linux provides discovery, basic reads, fixed bit selections, expressions, cycle sampling, protocol and transaction analysis, cross-format comparison and X history. The summary lists the currently accepted tools.

Signal tracing (driver, load, and connectivity path queries) follows Verdi NPI → Source Graph → basic static analysis (Legacy Static) by default. It first queries the elaborated KDB; when NPI is unavailable or cannot provide a trustworthy result, it tries Source Graph without a commercial license, then falls back to basic static analysis where supported.

Source Graph counts independent structural writers separately from assignment statements. All retained branch evidence remains available; traversal reports writer, assignment inspection, and evidence limits separately. Overlapping writers are structural evidence, not proof of an observed value conflict. Bit provenance explicitly distinguishes found, proved_no_driver, and unknown, with reason codes and driver_set_complete. A budget or unsupported write never proves absence. If Static also cannot handle an inconclusive Source Graph query, the bounded Source Graph evidence and both attempts remain visible. Driver-chain and provenance rows share identical metadata within a writer; statement_evidence retains every source line and column. The legacy source_line names the first entry. query_instance_path names the queried instance, while resolved_instance_path keeps its historical head-driver meaning. VCD searches explain that direction=null means unavailable metadata.

Driver output migration: explain_signal_driver now defaults to traceweave.driver.compact.v1. Each response contains all retained evidence, with identical statement semantics and ordered locations/indices shared in typed evidence_tables. Legacy scripts should request output_format="full" to retain the original JSON shape and indentation, or decode the response:

from src.evidence_output import expand_compact_result

driver = expand_compact_result(text_content.text) print(driver["driver_chain"])

Reading only response["result"] does not expand its references. Table entries and location occurrences are not writer counts. Other tools keep their defaults and traceweave.compact.v1 contract. Distinct evidence can still produce large responses; this format does not guarantee inline delivery by every client.

Hierarchy handles live only in the server process. An explicit driver compile_context can recover a missing handle once under a 30-second, 4,096-source-file, 128-MiB read budget (stricter hierarchy settings apply). hierarchy_recovery reports reuse, rebuilding or a blocker. Supplied identity tokens are revalidated; without a historical source fingerprint, recovery establishes current sources only. Structural scanning remains a separate step.

Capabilities for large designs, with selected examples of validated scale:

Hierarchy and source browsing on demand: the server builds and retains hierarchy and file indexes, then returns local results by instance, subtree, or file to keep large SoC queries manageable in the assistant's context. Hierarchy construction and local queries have been verified on a synthetic design with 50,500 logical instances; the initial build still scans compilation records and sources.
Bulk handshake checks: sweep_handshakes discovers AHB / valid-ready interfaces and checks stalls, payload stability, and premature valid/HTRANS deassertion. A recorded design with 78,817 total signals and a 2.59 ms waveform yielded 49 candidate interfaces involving 262 clock/protocol signals; 35 interfaces were checked in about 4.65 minutes, with 14 skipped and partial coverage. Runtime and coverage depend on the waveform, interface types, and resource limits.
GiB-scale log parsing: parse_sim_log has been verified on a 1 GiB synthetic log with 2,097,152 lines, finding all four errors at the beginning, middle, and end.

Installation

Requires Python 3.11+. Choose the installation that fits your environment:

Installation	Intended use
Repository	Simulation hosts with an existing Verdi installation, for FSDB, Source Graph, and optional NPI / LSF
PyPI	Log and VCD analysis, or Source Graph without a commercial license

Repository Installation

git clone https://github.com/gokeshenzhen/TraceWeave.git

cd TraceWeave export VERDI_HOME=/path/to/verdi bash scripts/install.sh

The installer prepares the Python environment, Source Graph, and FSDB reader, then checks the runtime. It leaves shell startup files and MCP client configuration untouched. FSDB support builds against the Synopsys FSDB Reader SDK in VERDI_HOME; TraceWeave does not redistribute its headers or libraries. Use of the FSDB Reader is subject to the applicable VC Apps Access Program Agreement (or successor). NPI and KDB operations also require the appropriate EDA runtime and license from your site.

For an existing installation, start with a read-only check:

bash scripts/install.sh --check

PyPI Installation

python3.11 -m pip install "traceweave-mcp[source-graph]"

traceweave-mcp --doctor

For logs, VCD, and basic static analysis only, install traceweave-mcp without [source-graph]. The PyPI package does not include the FSDB reader; use the repository installation for FSDB.

Optional FST reads

Install the extra in the MCP server's Python environment: python3.11 -m pip install "traceweave-mcp[fst]". For a repository installation, use .venv/bin/python -m pip install "pylibfst==0.2.1". Reconnect the server, then check traceweave-mcp --doctor or get_sim_paths. Existing VCD/FSDB users do not need this dependency.

For example, ask: “Find the FST waveform under /path/to/run, show its summary, find top.data, and read its transitions from 10 ns to 20 ns.” The basic tools preserve X/Z and distinguish missing recording intervals from observed values. Nonzero time offsets, real/string signals, external hierarchy files, and whole-file gzip containers are currently unsupported. Reads exceeding the bounded index, native memory, result, or time limits stop explicitly; narrow the query or use a smaller dump. See FST boundaries and resource limits.

Use the same analysis tools to inspect FST handshakes, reconstruct transactions, compare an FST signal against VCD/FSDB, or trace an earlier X after its input has recovered. Source-based analysis keeps its usual compile-context prerequisites. Recording gaps and incomplete clock history remain partial evidence.

Client Setup

After a repository installation, generate a client configuration template with absolute paths:

Client	Generate configuration template
Claude Code	`bash scripts/install.sh --print-config claude`
Codex	`bash scripts/install.sh --print-config codex`
Copilot	`bash scripts/install.sh --print-config copilot`

These commands only print templates. Add the output to your client's MCP configuration, supply any required site EDA environment variables, and reconnect the server. See the client configuration reference for complete examples.

Other MCP clients supporting stdio can use these connection settings:

Installation	`command`	`args`
Repository	`/.venv/bin/python`	`[" /server.py"]`
PyPI	`traceweave-mcp`, or its absolute path	`[]`

LSF-only NPI licenses

If Verdi/NPI licenses are available only on LSF compute nodes, provide these settings to the MCP server process. Replace digital with your queue:

export TRACEWEAVE_NPI_EXECUTION=lsf

export TRACEWEAVE_NPI_LSF_QUEUE="digital"

The client must inherit or explicitly pass these variables. Project files, the TraceWeave installation, and cache directories must be visible at the same absolute paths on submission and compute nodes. See the [LSF configuration reference](https://github.com/gokeshenzhen/TraceWeave/blob/main/docs/

Only the beginning is shown; read the rest in the repo

TraceWeave

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

🐙 TraceWeave

Use Cases

Installation

Repository Installation

PyPI Installation

Optional FST reads

Client Setup

LSF-only NPI licenses