The 'scan' tool has a name that does not clearly describe what it scans for. The description is adequate (107 chars, within baseline 34-392 range) but the input parameter lacks a description entirely. The tool has an input schema with types, but critically lacks any output schema documentation. The server provides no error handling guidance, no parameter constraints (e.g., max content length), and no recovery paths for edge cases. Tool name 'scan' is generic and does not follow verb_noun convention (should be 'scan_code' or 'scan_for_malicious_patterns'). This is a single-tool server with structural definition gaps that would significantly hinder LLM tool selection and chaining.

• Rename 'scan' to 'scan_code_for_threats' or 'detect_malicious_patterns' to clearly convey the tool's purpose and action.
• Add a description to the 'content' parameter: 'Source code content to scan for malicious patterns and security threats. Accepts up to 1MB of plain text or code in any language (Python, JavaScript, Bash, Go, etc.). Newlines preserved for line number accuracy.'
• Document the complete output schema. Define a response structure like: { 'findings': [ { 'pattern_id': string, 'severity': enum(critical|high|medium|low), 'name': string, 'description': string, 'matched_text': string, 'line_number': integer, 'risk_score': number } ], 'total_findings': integer, 'summary': string }.
• Add constraints to the 'content' parameter: 'Maximum 1MB (1048576 bytes). If content exceeds this, return error with guidance to split into multiple scans.'
• Add error handling examples in the tool description: 'Returns error if content is empty, exceeds 1MB, or is not valid text. Example error: "Content exceeds 1MB limit. Please split into multiple scans or contact support."'
• Consider adding an optional parameter 'language' (enum: python, javascript, bash, go, rust, java, etc.) to tune detection patterns and reduce false positives for language-specific attack vectors.
• Consider adding an optional 'severity_threshold' parameter (enum: critical, high, medium, low) to filter findings and reduce noise for different use cases.
• Migrate from STDIO to HTTP with Server-Sent Events (SSE) or native HTTP streaming to enable remote client connectivity and hosted deployment scenarios.