This MCP server has severe definition quality issues. Only 1 tool is defined (filter_copy). The tool has a basic description but critical gaps in schema completeness, parameter documentation, and error handling. The server appears to be a plain Python SDK wrapper with no visible MCP protocol implementation. Tool registration is not evident from the source code, only a Python class with a method is shown. Parameter validation and error guidance are entirely absent. Output schema is documented in code comments but not formally specified for MCP clients.

• Create a proper MCP server using the official MCP SDK (e.g., mcp-server-python). Explicitly register filter_copy as a tool with full JSON Schema for inputs and outputs.
• Expand tool description to 100-150 characters explaining: 'Evaluates text content for keyword stuffing and over-optimization by detecting common spam indicators (e.g., repeated promotional terms). Returns a spam risk score (0-100) and a verdict. Use before publishing marketing copy or user-generated content to catch over-optimized language.'
• Add parameter description for text_copy: 'The text content to evaluate (string, 1-10000 characters). Can be product descriptions, marketing copy, or article text. UTF-8 encoded.'
• Document output schema explicitly: {"spam_score": {"type": "integer", "min": 0, "max": 100, "description": "Risk score: 0-49 clear, 50-74 caution, 75-100 high risk"}, "is_safe": {"type": "boolean", "description": "True if spam_score < 75"}, "verdict": {"type": "string", "description": "Human-readable assessment and recommendation"}}}.
• Add error handling: return {"error": "Invalid input", "detail": "text_copy must be 1-10000 characters", "suggestion": "Trim or split the text and try again"} for out-of-range inputs.
• Document thresholds in the tool description: 'Scores below 50 are clear; 50-74 indicate caution; 75+ are high risk and warrant rewriting.'
• Optionally add constraints to the schema: specify text_copy minLength=1, maxLength=10000 to guide LLM input generation.