This is a Python SDK wrapper around an external API, not an MCP server. No MCP protocol implementation detected. Tool definitions exist in client.py but lack proper MCP registration, structured schemas, and comprehensive parameter documentation. All 14 tools have minimal descriptions (10-50 chars, well below the 194-char baseline). Parameter schemas are present but incomplete, many lack type constraints, enums, or validation guidance. No error handling patterns, no output schema documentation, and no tool composition support. This would require substantial refactoring to function as an MCP server.

• Implement as a proper MCP server using the MCP Python SDK (mcp package). Register tools via server.tool() decorator with full MCP protocol support.
• Expand all tool descriptions to 100 - 200 characters. Include WHAT the tool does, WHEN to use it (vs similar tools), and what it returns. Example: 'Extract structured data from a document (PDF, Word, image). Use when you need to parse forms, invoices, or contracts. Returns extracted fields with confidence scores and source citations.'
• Add comprehensive parameter descriptions with constraints. For 'fields' array, document the expected schema: 'Array of field objects, each with {name: string, type: enum[text|number|date|checkbox], required: boolean}'. For 'operations' array, list valid operations: 'Array of operations: {type: enum[resize|rotate|crop|filter], params: object}'.
• Document output schemas explicitly. For extract_document, specify: 'Returns {fields: [{name: string, value: any, confidence: 0-1, citations: [string]}], success: boolean}'. For generate_document, specify: 'Returns {buffer: bytes (base64), mime_type: string}'.
• Add error handling with recovery guidance. Catch API errors and return: 'Invalid document format. Supported: PDF, DOCX, PNG, JPG. Received: <actual_format>. Try convert_document_to_markdown() first if format is unsupported.' Classify errors as retryable (timeout, rate limit) vs user-fixable (invalid input).