llm-sca-tooling 0.16.0

Evidence-first, LLM-augmented static code analysis: graph indexing, MCP server, audit workflows

llm-sca-tooling

Evidence-first, LLM-augmented static code analysis.

llm-sca-tooling gathers typed evidence from your repository — AST symbols, call graphs, SARIF alerts, tests, runtime traces — grades it (parser > analyser > heuristic > unknown), and only then lets an LLM reason over the graded context. Findings carry their evidence grade; the tool never upgrades confidence on its own, and it never edits your source. Humans decide on remediation.

Five product surfaces

1. MCP server — the primary integration surface: 45+ tools, 14 resources, 12 prompts over stdio or Streamable HTTP. Point Claude Code (or any MCP client) at it and ask questions about your repository with evidence-graded answers.
2. Workflow orchestrator — bug resolve, patch review, SAST alert repair, implementation check (spec-vs-code compliance), and fault localisation. Every run produces a structured report, a run record, and a harness condition sheet.
3. Evaluation harness — a T1–T4 benchmark ladder with calibration reports; T1 runs in null mode (no LLM) and gates every CI check.
4. Operational harness plane — run records, incident tracking, trajectory memory with experience replay, operational reviews, release gates.
5. Operational guardrails — permission profiles, doom-loop and budget monitoring, governance-drift checks, privacy redaction, session replay.

Install

pipx install llm-sca-tooling          # CLI + MCP server
# optional capabilities:
pipx inject llm-sca-tooling fastembed # semantic retrieval (embeddings)
pipx inject llm-sca-tooling anthropic # live LLM boundaries (llm_mode)

or in a project:

pip install "llm-sca-tooling[embeddings,llm]"

Graph indexing uses universal-ctags (apt install universal-ctags / brew install universal-ctags).

Indexing backend tiers

Per language, facts are graded by the strongest backend that produced them and reconciled by confidence (a fact confirmed by several backends ranks higher):

Tier	Backends	Confidence
Semantic (cross-file resolution)	Python AST + pyan3, ts-morph (TS/JS), libclang (C/C++)	parser, 1.0
Real grammar (structural)	tree-sitter (Python, TS/JS, C/C++ — ships by default)	parser, 0.8
Heuristic	regex fallbacks, ctags	heuristic, 0.6–0.7

tree-sitter is always on (grammars are bundled dependencies); the semantic backends need their optional toolchain (Node/ts-morph, the cpp extra) and fall back through these tiers when absent.

Optional: full-fidelity TypeScript/JavaScript

The TypeScript/JavaScript backend uses ts-morph for parser-grade facts (resolved cross-file imports and call edges). It needs Node.js and a one-time dependency install:

cd <site-packages>/llm_sca_tooling/indexing/backends/typescript/runner
npm install

Without Node/ts-morph the backend transparently falls back to a heuristic regex parser — lower fidelity, but indexing still works.

Optional: full-fidelity C/C++

The C/C++ backend uses libclang (clang.cindex) for parser-grade facts (resolved symbols, #include edges, cross-file call edges). The cpp extra bundles a loadable libclang — no system toolchain needed:

pip install "llm-sca-tooling[cpp]"

It honours compile_commands.json at the repo root when present. Without libclang the backend falls back to the heuristic regex parser.

Quickstart

llm-sca-tooling config validate
llm-sca-tooling mcp serve --transport stdio   # start the MCP server

Register with Claude Code:

claude mcp add llm-sca-tooling -- llm-sca-tooling mcp serve

Then, from your MCP client:

register_repo(repo_path="/path/to/repo")
graph_build(repo_path="/path/to/repo")        # async — poll task_status
get_relevant_files(issue_text="NullPointerException in UserService.authenticate")
run_implementation_check(spec="<your architecture doc>", task=true)
run_issue_resolution(issue_text="<bug report>")

LLM mode

Workflows run in null mode by default — deterministic heuristics only, suitable for CI. With the llm extra installed and ANTHROPIC_API_KEY exported, pass llm_mode: true to run_implementation_check to enable the LLM boundaries (clause grounding, contract generation). Runs degrade to null mode when no provider is available and report llm_mode_active so a degraded run is never mistaken for an LLM-graded one. Model selection via LLM_SCA_MODEL (default claude-opus-4-8).

Evidence hierarchy

Grade	Source	Confidence
parser	ctags AST, language servers, exact SARIF locations	High
analyser	dataflow, cross-file call graph	Medium-high
heuristic	pattern matching, keyword search, LLM-classified groundings	Medium
unknown	no hard evidence — reported honestly, never auto-passed	Low

Every LLM-derived artefact is fail-closed: unparseable output degrades to the deterministic null path, citations are filtered to evidence actually provided, generated predicates must compile before they count, and LLM groundings carry a derivation="llm" audit field.

Governance

Development of this repository is governed by AGENTS.md: hard constraints (no secrets, write allowlists, deny-by-default egress), a verify-before-commit gate (make verify), live session telemetry, and harness condition sheets for every evaluation and release run.

Documentation

• Installation · Quickstart · Architecture
• Evaluation guide · Harness setup · Plugin authoring · Incident response
• CHANGELOG

License

MIT