scan_snippet01Review a snippet
Send selected code with its language and get a focused security analysis, with an explanation and suggested changes.
winfunc_mcp.scan_snippet( code="def login(user, pw): ..." language="python")Ask for a focused code review, compare a change, or bring file context into a security investigation, without opening another dashboard. The output gives your engineer more context; it never silently approves or ships a change.
The recording shows the interaction in a supported AI editor. Results depend on the code you supply.
Each action sends explicit code context to the configured winfunc endpoint and returns material for an engineer to assess.
scan_snippet01Send selected code with its language and get a focused security analysis, with an explanation and suggested changes.
winfunc_mcp.scan_snippet( code="def login(user, pw): ..." language="python")scan_codebase02Provide the file content and its path so the review can reason about more than an isolated line.
winfunc_mcp.scan_codebase( file_path="app/models/user.py" file_content="...")verify03Review a before-and-after change for security regressions before it moves forward.
winfunc_mcp.verify( original_code="# before" code_snippet="# after" language="python")Generate a key in the winfunc app. Keys are stored only as SHA-256 hashes and can be revoked at any time.
Paste the configuration into your editor's MCP settings. stdio and SSE transports are both supported.
Run a review on non-sensitive code first, then roll the workflow out to the team.
mcp.json
stdio · replace YOUR_API_KEY
{ "mcpServers": { "winfunc-mcp": { "command": "pipx", "args": [ "run", "winfunc-mcp-server", "--api-url", "https://api.winfunc.com", "--transport", "stdio", "--key", "YOUR_API_KEY" ] } }}Start on representative code and judge the quality of the evidence and suggested changes before wider adoption.
Try the MCP workflow, or talk with us about language, client, and data-handling requirements.