This MCP server exposes three tools for code analysis. All three tools have descriptions and declared input parameters, but critical quality gaps exist: (1) Parameter descriptions are minimal or absent, most input parameters lack explanation of what they control or what values are valid. The 'scan_mode' parameter in issue_detection_review is described as '"quick" (fastest tools only) or "deep" (all tools + LLM). Default is "quick".', which is adequate but bare. The 'code_directory' and 'output_directory' parameters across all tools lack descriptions entirely in the visible schema. (2) Output schemas are undocumented, none of the three tools declare what fields or structure they return, forcing LLMs to infer the result shape. (3) Tool naming is reasonable ('issue_detection_review', 'documentation_generate', 'comprehensive_review') and action-oriented, but descriptions are generic and do not explain interdependencies or when to use each tool instead of the others. (4) No input validation guidance, error handling patterns, or recovery hints are present. The server appears to lazy-load agent modules (good for startup latency) but provides no input constraints (enums, limits, formats) in the visible code. Overall, this is a functional but minimally documented MCP server, typical of early-stage internal tools rather than production-grade agent integrations.

• Add non-trivial descriptions to every parameter. For 'code_directory': 'Absolute or relative path to the root of the codebase to analyze. If omitted, defaults to the current working directory.' For 'output_format': 'Output format for the analysis report. Valid values: "md" (Markdown, default), "json" (JSON structure).' For 'scan_mode': 'Scanning depth. "quick" runs fast static checks only; "deep" runs all checks plus LLM-based reasoning (slower but more thorough).'
• Document return types and schemas for each tool. E.g., 'Returns a dict with keys: {"status": "success"|"error", "report": {...}, "issues_found": int, "output_file": str (path to generated report)}'. This allows LLMs to extract and chain results.
• Convert 'output_format' and 'scan_mode' to enum types in the schema. If using FastMCP, ensure the @mcp.tool() decorator or parameter hints enforce enum validation: Literal["md", "json"] and Literal["quick", "deep"].
• Clarify tool roles in descriptions. Add a line to each: 'Use issue_detection_review for focused issue analysis only. Use documentation_generate for codebase documentation only. Use comprehensive_review to run both analyses together.' This guides LLM selection.
• Add error handling examples to docstrings: 'Raises: FileNotFoundError if code_directory does not exist. PermissionError if output_directory cannot be written. ValueError if scan_mode is invalid.' Then implement recovery guidance in error responses.
• Make output_directory explicit and safer: add a note 'If omitted, defaults to {code_directory}/DOCUMENTATION. Pass explicitly to avoid modifying the source tree.' or change the default to a temporary directory.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry