Pyslang MCP Server

pyslang-mcp

pyslang-mcp is a local Model Context Protocol server that gives AI agents compiler-backed, read-only context for Verilog and SystemVerilog projects.

It wraps pyslang so an MCP client can ask questions against parsed and elaborated HDL instead of plain text:

• What modules, interfaces, and packages are in this filelist?
• What diagnostics does the compiler frontend report?
• What is the instance hierarchy below this top?
• Where is this symbol declared or referenced?
• Did my include paths, defines, and nested .f files resolve as expected?

This is not a simulator, synthesizer, waveform viewer, linter replacement, or RTL refactoring tool. It is a small semantic analysis service for local HDL checkouts.

That read-only boundary is intentional. It keeps the server side-effect free, reduces the blast radius in IP-protected workspaces, and makes it safer to use in local and CI environments.

It was decided to keep pyslang-mcp read-only. The LLM handles the actual RTL coding, while pyslang-mcp serves as the compile/elab-backed reader, checker, and explainer for code that already exists. In other words, the model drafts the RTL and the MCP server verifies what the compiler frontend sees.

[!NOTE] The project is currently early-stage and published on PyPI and the MCP Registry for local stdio use.

Why ASIC And EDA Engineers Might Care

Most AI coding tools are good at searching text. HDL usually needs more than that.

In real projects, useful answers often depend on filelists, packages, includes, defines, generate blocks, and hierarchy. pyslang-mcp gives an agent a compact compiler-backed view of that structure, while keeping the server read-only and scoped to a project root you provide.

Good fits:

• triaging parse and semantic diagnostics before asking an agent to reason about RTL
• checking what a .f file expands to
• listing design units in a block or small IP
• finding a declaration without chasing comments and stale grep hits
• getting hierarchy and port-connection context for review/debug prompts
• giving workflow agents HDL context without handing them an EDA runtime

Quickstart

Install the package:

pip install pyslang-mcp

Run the local stdio server:

pyslang-mcp

For contributor setup, clone the repo and install it in editable mode:

git clone https://github.com/ariklapid/pyslang-mcp.git
cd pyslang-mcp
python -m venv .venv
./.venv/bin/pip install -e '.[dev]'

Run the checkout stdio server:

./.venv/bin/python -m pyslang_mcp

The installed console script works too:

./.venv/bin/pyslang-mcp

Run tests:

./.venv/bin/pytest

MCP Client Config

Use local stdio. The MCP client should launch the server on the same machine, VM, or dev container that can see your RTL checkout.

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

For editable checkout installs, point the command at the checkout virtual environment instead:

{
  "mcpServers": {
    "pyslang-mcp": {
      "command": "/absolute/path/to/pyslang-mcp/.venv/bin/python",
      "args": ["-m", "pyslang_mcp"]
    }
  }
}

Tool calls must provide a project_root. Source paths, filelists, and include directories may be absolute or relative, but they must stay under that root.

Minimal Tool Payloads

Analyze explicit files:

{
  "project_root": "/path/to/rtl-project",
  "files": ["rtl/pkg.sv", "rtl/top.sv"],
  "include_dirs": ["include"],
  "defines": {
    "WIDTH": "32"
  },
  "top_modules": ["top"]
}

Analyze a filelist:

{
  "project_root": "/path/to/rtl-project",
  "filelist": "compile/project.f"
}

Find a symbol:

{
  "project_root": "/path/to/rtl-project",
  "filelist": "compile/project.f",
  "query": "payload",
  "match_mode": "exact",
  "include_references": true
}

Tools

Typical flow:

1. Start with pyslang_parse_filelist or pyslang_parse_files.
2. Run pyslang_get_diagnostics.
3. Use pyslang_list_design_units to see what the compiler frontend found.
4. Use pyslang_describe_design_unit, pyslang_get_hierarchy, or pyslang_find_symbol for the actual review/debug question.

Filelist Support

The current parser intentionally supports a practical subset used by many RTL flows:

• source file entries
• nested filelists with -f and -F
• include directories with +incdir+..., -I dir, and -Idir
• defines with +define+..., -D NAME, and -DNAME

Unsupported filelist tokens are reported in the tool output instead of being silently ignored.

Example Agent Prompts

Use this server when compiler-backed context matters:

• "Parse compile/project.f with +define+DEBUG and group diagnostics by source file."
• "List every design unit in this project and identify the likely top modules."
• "From top, show the instance hierarchy down to depth 4."
• "Describe module axi_dma_top: ports, child instances, and declared names."
• "Find the declaration and references for payload_valid."
• "Confirm whether legacy_widget is instantiated anywhere the elaborator sees it."
• "Show the resolved files, include dirs, defines, and unsupported entries from this filelist."

For single-line questions, comments, naming searches, or partial/incomplete source sets, regular rg, editor search, or direct file reading is usually faster and clearer.

Guardrails

• Read-only MCP tools. No RTL edits, formatting, simulation, or synthesis.
• Strict project-root scoping. Paths outside project_root are rejected.
• Compact JSON responses with truncation metadata for large result sets.
• Process-local cache keyed by project config and tracked file mtimes.
• pyslang_preprocess_files is summary-oriented. It returns preprocessing metadata and source excerpts, not a guaranteed full standalone preprocessed stream.
• streamable-http remains experimental. The internal MaaS path wraps it with a bearer token for single-server use, but it is not a complete production hosted security boundary by itself.

Remote MaaS Direction

Remote MaaS is planned as two separate tracks, not as one generic hosted mode:

• Plan A: public OSS MaaS. A convenience/demo service for public open-source HDL repositories. This is not suitable for proprietary or confidential RTL.
• Plan B: internal MaaS. A self-hosted deployment pattern for companies to run inside their own network, close to internal repositories, auth, logging, and security controls.

Start with the internal MaaS quickstart for a single internal server. See REMOTE_DEPLOYMENT.md for the full Plan A / Plan B split.

Docker Compose is the recommended bring-up path because it avoids Python setup issues and mounts the RTL checkout read-only. Docker is not required; the quickstart also includes a native Python path for corporate servers where containers are not available.

HDL Example Corpus

The repo includes generated HDL examples under examples/hdl:

• clean reference projects from single modules to small IP-style examples
• intentionally buggy variants labeled easy, medium, and hard
• local validation hooks for both pyslang and verilator --lint-only

Run the full corpus validator:

./.venv/bin/python scripts/validate_hdl_examples.py

Run the CI smoke subset:

./.venv/bin/python scripts/validate_hdl_examples.py --smoke-only

Using With Broader RTL Skills

If your agent environment already has a broad RTL skill such as rtl-analysis, rtl-audit, or llm-based-verilog-analysis, use pyslang-verilog-context as the fine-grained compiler-evidence layer beneath that broader skill.

Recommended setup:

1. Configure the pyslang-mcp server in your MCP client so the agent can call the read-only pyslang_* tools.
2. Install or copy the pyslang-verilog-context skill from skills/pyslang-verilog-context.
3. Add a delegation note like this to your broader RTL skill:

For Verilog/SystemVerilog tasks, use $pyslang-verilog-context before making
claims about diagnostics, design units, ports, hierarchy, declarations,
references, includes, defines, or filelist behavior. Treat its output as
compiler frontend evidence only. Do not present it as simulation, synthesis,
timing, CDC/RDC, formal, or full lint signoff.

The intended flow is:

user RTL request
  -> broad RTL analysis/audit skill
  -> pyslang-verilog-context
  -> pyslang-mcp compiler-backed evidence
  -> final answer with evidence and limitations

This keeps high-level RTL review policy in the broad skill while grounding Verilog/SystemVerilog structure, diagnostics, hierarchy, symbols, includes, and filelists in pyslang.

Evaluation Benchmarks

The repo includes a pyslang-verilog-context skill eval suite under skills/pyslang-verilog-context/evals and comparison reports under reports/. The current benchmark compares three local evidence modes:

• text/no skill: source and filelist text heuristics only
• MCP/no skill: targeted pyslang-mcp tool calls
• skill + MCP: pyslang-verilog-context sequencing with pyslang-mcp

Latest local result from 2026-05-23:

The benchmark mixes deterministic repo-local HDL fixtures with real public RTL source files. The local fixtures exercise targeted behaviors such as filelists, hierarchy, symbols, diagnostics, clean-frontend functional bugs, and RTL coding and bug-audit discipline. The real public RTL cases use source files from lowrisc-ibex, pulp-common-cells, verilog-axis, pulp-axi, pulp-register-interface, and picorv32. They exercise frontend diagnostic status, design-unit inventory, and first-unit port counts on real source files. See docs/evaluation-benchmarks.md for the prompt/task shapes and MCP functions used.

Verification commands used for the reported run:

./.venv/bin/python skills/pyslang-verilog-context/scripts/validate_eval_fixtures.py
./.venv/bin/python -m pytest
./.venv/bin/python skills/pyslang-verilog-context/scripts/run_comparison_evals.py
./.venv/bin/python scripts/run_mcp_comparison.py --output-dir reports/mcp_comparison_comprehensive_20260523
./.venv/bin/python reports/real_examples_75/run_real75_comparison.py
./.venv/bin/python -m py_compile reports/real_examples_75/run_real75_comparison.py

These are deterministic scalar harnesses, not blind autonomous LLM-judge runs. pyslang-mcp evidence remains frontend/compiler context only, not simulation, synthesis, CDC/RDC, timing, formal, or full lint signoff.

Documentation-only eval/report updates do not require a PyPI release. A PyPI release is warranted when package code, public tool behavior, schemas, runtime dependencies, CLI behavior, or user-facing install/runtime docs change in a way published users need.

Project Status

Implemented:

• FastMCP stdio server
• CLI entrypoint via python -m pyslang_mcp and pyslang-mcp
• project loader with root checks and .f parsing
• pyslang-backed diagnostics, design-unit listing, hierarchy, symbol lookup, syntax summaries, and project summaries
• bounded in-memory cache
• fixture-backed tests and Ubuntu CI for Python 3.11 and 3.12
• package smoke CI from a built wheel
• manual PyPI Trusted Publishing release workflow with release-gate tests
• PyPI package: pyslang-mcp
• MCP Registry entry: io.github.ariklapid/pyslang-mcp
• internal MaaS bring-up artifacts: Dockerfile, Docker Compose config, native Python fallback instructions, systemd template, bearer-token HTTP option, and a single-server quickstart

Not done yet:

• schema freeze for a more mature API-stable release
• broad platform validation beyond the current Linux-focused CI path
• production MaaS hardening for broad team use: SSO, multi-workspace routing, Kubernetes deployment, source-safe metrics, and reverse-proxy examples

Development

Useful commands:

./.venv/bin/ruff check .
./.venv/bin/pyright
./.venv/bin/pytest

Architecture and contribution docs:

• docs/architecture.md
• docs/internal-maas-quickstart.md
• REMOTE_DEPLOYMENT.md
• docs/mcp-registry.md
• CONTRIBUTING.md

License

Apache-2.0. See LICENSE.