reachscan

Static capability analysis for Python and TypeScript/JavaScript AI code. Know what it can do before it does it.

Quick start

The problem

You're giving an LLM tools. Tools mean real-world access — files, shell, network, credentials.

Most developers add tools without a clear accounting of what permissions they're actually granting. The agent docs tell you what the tool is for. They don't tell you what it can do.

reachscan is the accounting.

It analyzes Python and TypeScript/JavaScript code and reports the actual capabilities present: what the code can read, write, execute, send, and access. Not what the README says. What the code does.

What it detects

Seven capability classes, built from AST analysis of Python and TypeScript/JavaScript code:

Cross-capability risks are also flagged when both capabilities are reachable: READ + SEND (secret leakage), SEND + WRITE (data exfiltration), EXECUTE + SEND (remote control), and EXECUTE + destructive WRITE (destructive agent).

Reachability analysis

Knowing a capability exists in a codebase is useful. Knowing whether the LLM can actually trigger it is what matters.

reachscan detects the LLM-facing entry points in your codebase, builds an intra-project call graph, and traces which capabilities are reachable from those entry points. Every finding is tagged with one of five states:

The call graph follows up to 8 hops from each entry point. Call paths are shown in the report so you can see exactly how the LLM reaches a capability.

Known limitations of the Python call graph. It resolves:

• plain calls to project functions (helper(), including ones imported from another project file);
• self.method() calls to methods defined in the same class and file;
• module.function() calls.

It doesn't yet resolve method calls on object instances (client.fetch(), self.db.query()), chained attribute calls (a.b.c()), super().method(), or inherited methods. A capability reached only through such a call shows as unreachable or unknown, and can't produce an annotation mismatch.

Entry point detection — Python

reachscan recognises LLM-callable functions across all major Python agent frameworks:

Framework attribution uses a confidence-graded resolution chain: direct imports are resolved at 0.95 confidence, inferred instance variables (e.g. weather_agent = Agent[Deps, T](...)) at 0.80, and unresolvable decorator names fall back to the best available label at 0.60.

Python entry points feed into the reachability pass — the call graph is traced from each detected entry point to identify which capabilities the LLM can actually trigger.

Entry point detection — TypeScript and JavaScript

reachscan parses .ts, .tsx, .js, .jsx, .mts, .mjs, .cts, and .cjs files with tree-sitter, using prebuilt Python wheels, so no Node.js runtime is required. Comments and strings don't produce entry points. If a file can't be parsed (for example, TypeScript syntax newer than the bundled grammar), reachscan falls back to regex matching for that file.

Registration calls are matched however they're formatted. Declaration files (.d.ts), test files, minified bundles, and node_modules/dist/build directories are automatically excluded.

TypeScript and JavaScript code is also analyzed for capabilities: child_process and execa (EXECUTE), fs and fs/promises (READ/WRITE), fetch, axios, got, undici, http(s), net, and WebSockets (SEND), process.env, dotenv, and keytar (SECRETS), eval, new Function, vm, and non-literal import()/require() (DYNAMIC), and setInterval, cron libraries, and worker threads (AUTONOMY). Imports are resolved first, so regex.exec() or a local exec helper isn't mistaken for child_process.exec.

TypeScript reachability works like Python's. Each tool's handler is an entry node, and the call graph follows direct calls to functions in the same file, relative imports (ESM, including ./x.js → x.ts, CommonJS require, and namespace imports), this.method() within a class, and methods of object literals. Callbacks defined inside a function are treated as part of it. Method calls the graph can't resolve, such as tool.execute() on a class instance or on a tool object taken from a list, aren't followed: code that only such a call could reach is unknown. Other code that isn't on a path, including code reached only through computed calls (table[name](...)) or only from top-level code (for example the constructor of a module-level singleton), is unreachable. Path depth, states, and exit codes match the Python analysis.

Handlers are found for the patterns above, and also for: addTool(toolObject) with a tool object defined in the project; tool-definition objects using schema, parameters, or args instead of inputSchema, with a handler or execute property or method (for example defineTool({ ..., handler })); objects passed to a project wrapper that itself calls registerTool / tool / addTool; xmcp file-based tools (a file exporting metadata and a default function, in projects that depend on xmcp); and server.tool(...) / registerTool(...) calls whose name isn't a literal (reported with the name unknown, in files that import an MCP SDK).

Verifying MCP tool annotations

MCP tools can declare ToolAnnotations hints such as readOnlyHint, openWorldHint, and destructiveHint. Clients use them to decide what to auto-approve, but they're claims the server makes about itself. For Python MCP servers, reachscan checks each claim against what the tool can actually reach:

Annotation Mismatches  —  MCP tool annotations contradicted by reachable code
-----------------------------------------------------------------------------
  [HIGH] get_report declares readOnlyHint: true
    but reaches WRITE via os.remove() (server.py:9)
    path: get_report → _cleanup → os.remove()

Every mismatch comes with the call path from that tool to the contradicting code. A contradiction without such a path isn't reported. Only explicitly declared hints are checked: absent hints fall back to the spec's conservative defaults, which claim nothing, and hints reachscan can't resolve statically (imported from outside the project, built by a helper function) are skipped and listed under --explain. FastMCP @mcp.tool(annotations=...) and lowlevel types.Tool(...) declarations are both supported. Lowlevel tools are linked to their branch in the call_tool handler when it dispatches with if name == ... or match name:. Mismatches appear in the text report, in JSON (annotation_mismatches, schema 1.1), and in SARIF as rule mcp-risk-mismatch. TypeScript support is planned.

What it looks like

Agent Capability Report
=======================

Python Entry Points (LLM-controlled surface)
----------------------------------------------
  • get_lat_lng  (pydantic_ai/decorator @ weather_agent.py:50)
  • get_weather  (pydantic_ai/decorator @ weather_agent.py:67)

Capabilities
------------
  • SEND

Combined Risks
--------------
  None inferred from combined-capability rules.

Reachability Summary
--------------------
     3 reachable     — LLM can trigger these directly
   117 unreachable   — exist in codebase, not on any LLM call path
     3 module-level  — execute on import, not on any call path

Reachable Findings  —  LLM can trigger these directly
------------------------------------------------------
  [HIGH] SEND via ctx.deps.client.get -> https://api.weather.example.com (network @ weather_agent.py:58)
    path: get_lat_lng
    explanation: This code can send data over the network to external services.
    impact: Sensitive local data could be transmitted to untrusted endpoints.

Other Findings  —  not on LLM call path
-----------------------------------------
  [HIGH] UNREACHABLE  SECRETS via os.getenv('ANTHROPIC_API_KEY') (secrets @ model_client.py:12)
    explanation: This code accesses secrets or credential sources.
    impact: Credentials may be disclosed and used for unauthorized access.

  [HIGH] MODULE_LEVEL  SECRETS via os.getenv('PYDANTIC_AI_MODEL') (secrets @ config.py:25)
    reachability: Executes on import — runs whenever this module loads
    explanation: This code accesses secrets or credential sources.
    impact: Credentials may be disclosed and used for unauthorized access.

You get file paths and line numbers. Not just "this repo uses subprocess" — you get exactly where, how, and whether the LLM can reach it.

Who needs this

Agent developers — audit your own code before shipping. Know exactly what you're granting the LLM access to, and where those grants live in your codebase. Add the GitHub Action to catch new reachable capabilities in every pull request.

Security and platform teams — you're deploying agents your developers wrote, or agents that use third-party frameworks. Before they hit production, run a scan. Get a fast, defensible answer to "what can this thing actually do?"

Anyone integrating third-party tools — tools, plugins, and MCP servers come with capabilities attached. Scan them before wiring them into your agent. reachscan https://github.com/some-org/some-tool takes seconds and requires nothing installed on that repo, or ask your coding agent to do it with the Claude Code / Codex plugin.

MCP server authors — show your users exactly what your server can and cannot do. A clean scan result is a trust signal, and the GitHub Action keeps it clean as the server changes.

It's not just for agents

The name is intentional but the scope is broader.

Any Python or TypeScript/JavaScript code that runs in an AI-adjacent context is a valid target — tool libraries, retrieval pipelines, memory modules, execution sandboxes. If an LLM can call it, you want to know what it can do.

Precision

Detection quality was validated in a structured false positive audit across 10 major open-source agent repos (AutoGPT, LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, Autogen, pydantic-ai, agentops, anthropic-cookbook, python-sdk) — approximately 3,900 labeled findings:

Low noise by design. When it fires, it's real.

This audit covers the Python detectors. The TypeScript/JavaScript detectors are newer and will get their own audit.

What this is NOT

• Not a vulnerability scanner
• Not a linter
• Not a dependency checker
• Not a compliance tool
• Not a prompt injection detector

It is a capability audit. Static analysis only — results describe what the code is capable of, not what it will do in any given execution.

Installation

Option 1 — Recommended (install as a CLI tool)

pipx install reachscan

Or with pip:

pip install reachscan

Then run:

reachscan .

Option 2 — Install from source (development)

git clone https://github.com/vinmay/reachscan.git
cd reachscan

python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate

pip install -e .[dev]

Option 3 — Run without installing

python -m reachscan.cli examples/demo_agent

Use it from Claude Code or Codex

The reachscan plugin adds a vet-mcp-server skill to your coding agent. Before you add an MCP server, ask:

vet this MCP server before I add it: https://github.com/org/some-mcp-server

The agent runs reachscan on the server and tells you what each tool can execute, read, write, and send, with call paths for high-risk findings and an Install / Review first / Avoid verdict based only on the scan results. The plugin needs the reachscan CLI on your PATH (pipx install reachscan) and a terminal, so it works in Claude Code and Codex.

Claude Code (inside a session):

/plugin marketplace add vinmay/reachscan
/plugin install reachscan@reachscan

Codex:

codex plugin marketplace add vinmay/reachscan

Then open the Plugins Directory, choose the reachscan marketplace, and install the plugin. Details are in integrations/agent-plugins.

Requirements

• Python 3.11+
• pip or pipx

Usage

reachscan [target] [--json | --sarif] [--severity {high,medium,none}] [--explain]

target accepts:

The GitHub URL path does a shallow clone — you don't need the repo checked out locally.

Exit codes

--severity flag

Controls when the CLI exits 1:

--explain flag

Expands the call chain for every reachable finding, showing each hop with its source file. Use this when you want to understand exactly how the LLM reaches a capability — not just that it can, but through which functions.

Without --explain:

  [HIGH] DYNAMIC via exec() (dynamic_exec @ addon.py:431)
    path: execute_blender_code → … → execute_code

With --explain:

  [HIGH] DYNAMIC via exec() (dynamic_exec @ addon.py:431)
    call chain:
      execute_blender_code @ server.py
      → send_command @ server.py
      → execute_code @ addon.py

Only applies to the text report. Has no effect with --json or --sarif.

--sarif flag

Writes SARIF 2.1.0 to stdout, for GitHub code scanning and other SARIF viewers. It can't be combined with --json, and exit codes are the same.

• One rule per capability (reachscan/EXECUTE, reachscan/SEND, ...) and one per combined risk (reachscan/combined/remote_control, ...).
• Levels: a reachable high-risk finding is error, a reachable medium-risk finding is warning, and everything else is note.
• Each reachable finding has a codeFlow that walks the call chain from the LLM entry point to the sink, so the code scanning UI shows the path step by step.
• Reachability state, confidence, and entry point are in each result's properties.
• By default only reachable and module_level findings are included, so the Security tab shows only what an LLM can trigger. Add --sarif-include-unreachable to include everything.
• When a language has findings but no detected entry points, reachability isn't evaluated for it and its findings are left out by default. The SARIF run then carries a warning notification (invocations[].toolExecutionNotifications) saying how many findings weren't shown, so an empty Security tab isn't mistaken for a clean scan.

CI Integration

The easiest way is the reachscan GitHub Action. It installs reachscan, uploads findings to the GitHub Security tab with call chains, and fails the job when a reachable finding meets your severity threshold:

name: reachscan
on: [push, pull_request]
permissions:
  contents: read
  security-events: write
jobs:
  reachscan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v7
      - uses: vinmay/reachscan-action@v1
        # with:
        #   severity: medium   # or none for report-only
        #   path: servers/my-mcp-server

Inputs, outputs, and more examples are in the action's README.

To run the CLI yourself instead, for example to keep a JSON report as a build artifact:

- uses: actions/checkout@v7
- run: pipx install reachscan
- name: Run capability audit
  run: reachscan . --json > reachscan-report.json
  # Exits 1 if HIGH reachable capabilities found
- uses: actions/upload-artifact@v7
  if: always()
  with:
    name: reachscan-report
    path: reachscan-report.json

To audit without blocking the pipeline (report only):

- run: reachscan . --json --severity none > reachscan-report.json

To upload SARIF to the GitHub Security tab without the action:

permissions:
  security-events: write
  contents: read
steps:
  - uses: actions/checkout@v7
  - run: pipx install reachscan
  - name: Run reachscan
    run: reachscan . --sarif > reachscan.sarif
  - uses: github/codeql-action/upload-sarif@v4
    if: always()
    with:
      sarif_file: reachscan.sarif
      category: reachscan

Project direction

The goal:

Give AI systems a permission model they've never had.

Static capability detection is the foundation. Reachability analysis on top of it answers the harder question: not just can this code do something, but can the LLM trigger it.

Status

What works today:

The JSON output schema is stable at v1 — see docs/schema_v1.md for the full field reference. Feedback, edge cases, and false positive reports are especially valuable — open an issue.

Support

If reachscan is useful to you, star the repo — it helps others find it and tells us people care about this problem.