re-angr
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@re-angrbuild control flow graph for function 'main' in /bin/ls"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
re-angr
MCP server for angr (UC Santa Barbara, BSD) — symbolic execution + CFG + reaching-definitions. The "I have a heavy constraint problem and want a second opinion" tool.
Why
The other RE-AI MCP servers handle most RE tasks well. But there are two cases where angr's approach is materially different from re-triton's:
CFG construction. angr's CFG is built statically (no execution, no emulation) and is one of the most accurate in the open-source world.
re-rizin.analyze_functionis faster but uses different heuristics; cross-validating with angr catches edge cases.Reaching definitions. angr's dataflow analysis is what makes
re-mba-deobfuscatereliable. An MBA identity likex + y == (x & y) + (x | y)looks like real arithmetic, but the reaching-defs graph reveals that(x & y)and(x | y)were defined from the same source — the identity is a no-op substitution.
re-angr exposes both, and re-triton for the same constraint problem. The two together give the analyst a much stronger signal than either alone.
Related MCP server: angr-mcp
Architecture
The Python MCP server is a thin wrapper around an angr-cli Python helper installed by install.sh:
Claude Code (MCP stdio)
│
▼
re-angr server (Python, this directory)
│ subprocess.run(...)
▼
angr-cli (small Python script, wraps the angr API)
│
└─ angr>=9.2 (pip-installed, the actual binary analysis platform)The subprocess boundary is intentional: angr keeps long-lived AngrProject objects, which don't survive across JSON-RPC calls cleanly. The helper spawns a fresh Python process per call, which matches the per-tool-call model the MCP server uses.
Tools
Tool | What it does |
| Health check — return angr + cle versions |
| Build a control-flow graph of a binary (or one function) |
| Run angr symbolic execution starting at an address |
| Compute the def-use graph for a function |
Install
./install.sh installs angr>=9.2 from PyPI.
To install standalone:
pip install 're-angr[core]'Requirements
Python 3.11+
angr>=9.2 (BSD, on PyPI)
No system dependencies
Degraded mode
If angr-cli is not installed, every tool returns {"status": "WARN", "error": "angr-cli not installed; run install.sh", ...}. The Python MCP server itself always loads so Claude Code can surface the install hint.
Pairing with re-triton
re-triton is the fast first-call symbolic executor (Triton 1.0 with Quarkslab's bindings). re-angr is the cross-validation pass:
Use
re-triton.solve_constraintfor "what input reaches this branch?" (fast, in-process).Use
re-angr.symbolic_execfor "does another symbolic executor agree?" (slower, but independent).Use
re-angr.reaching_definitionsfor "where was this variable defined?" (Triton doesn't compute this — it's a static dataflow analysis).
For MBA-obfuscated arithmetic: re-triton.solve_constraint solves the symbolic equation; re-angr.reaching_definitions shows the variable is a no-op substitution. Both together is a strong "this is a known identity" signal.
For CFG construction: re-rizin.analyze_function is the fast first call; re-angr.build_cfg is the cross-validation pass. Discrepancies (e.g. angr sees an indirect call that rizin doesn't) are worth investigating.
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