tasie-mcp
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., "@tasie-mcpScan this code for vulnerabilities and suggest fixes"
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.
TASIE MCP
TASIE — autonomous SAST + real exploit + human-gated remediation — exposed as an MCP app (NitroStack + NitroCloud), so ChatGPT and other MCP clients can scan code for vulnerabilities and get proof-carrying remediation.
This Node/TypeScript service is a thin MCP surface over the TASIE FastAPI
backend (demo/backend/). All detection, live exploitation and patching happens
in the Python engine; this app forwards tool calls over HTTP.
Architecture
ChatGPT / Studio ──MCP──▶ tasie-mcp (this app) ──HTTP──▶ TASIE FastAPI backend
(~90 engines + Docker
exploit sandbox)The backend URL is TASIE_API_BASE (default http://localhost:8000).
Stateless tools (
scan_code,scan_dependencies,detect_frameworks) send the code/manifest inline — they work against any reachable TASIE host.Backend-state tools (
remediate_file,scan_repo) act on files that live on the TASIE host and need the full Docker-enabled deployment.
Related MCP server: chatgpt-codex-local-mcp
Tools
Tool | Backend route | Needs Docker host | Purpose |
|
| no | Static scan of inline source → ranked findings (renders scan-report widget) |
|
| no | SCA over an inline dependency manifest |
|
| no | Fingerprint web framework(s) + attack surface |
|
| yes | Detect → live-exploit → patch → re-verify a host file |
|
| yes | Route discovery + scan of a repo on the host |
|
| no | Backend reachability + which endpoint is wired |
Setup
npm install # main app
npm --prefix src/widgets install # widget subpackage (or run `npm run dev`)
cp .env.example .env # then set TASIE_API_BASEStart the TASIE backend first (from demo/backend):
.venv/Scripts/python.exe -m uvicorn app:app --host 127.0.0.1 --port 8000Run
npm run dev # MCP server (STDIO) + widget dev server (:3001)Then connect in NitroStudio → Add Server → Nitro Project → this folder →
Studio App Canvas. Test scan_code on the Tools page; the TASIE Scan Report
widget renders the findings.
Deploy (NitroCloud → ChatGPT)
Set
TASIE_API_BASEto a publicly reachable TASIE host (a cloud VM/Docker host runningdemo/backend). NitroCloud deploys only this Node app — the Python engine + exploit sandbox must be hosted separately.In Studio App Canvas: Link to app / Create Cloud App → Deploy.
In ChatGPT: Settings → Plugins → Developer mode (Plus/Pro) → + → Server URL =
{serviceUrl}/sse→ Create → Connect.
Cloud builds run on Node 20 (
enginespins20.x). Local dev on newer Node works but test against 20 before deploying.
Config
Var | Default | Meaning |
|
| TASIE backend base URL |
| — | optional bearer token ( |
|
| per-request timeout |
Notes
ctx.loggeronly inside tools —console.*breaks the STDIO transport.Verified end-to-end:
scan_codeon a Flask SQLi sample returnscritical / CWE-89 / OWASP A03from the live backend.
Maintenance
Resources
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