This is a Python client library, not an MCP server. It lacks transport layer, MCP protocol implementation, and any server registration mechanism. The single 'audit' tool has a reasonable input schema and description, but the codebase shows no evidence of MCP tool registration, error handling patterns, or any protocol-layer work. The audit() method exists as a Python class method in client.py, but there is no explicit tool definition block, no MCP server scaffolding, no resource/prompt support, and no transport configuration. The overall definition quality score is pulled down further by lack of error handling guidance, no security patterns, no idempotency markers, and no output schema documentation.

• Wrap this client library in an MCP server implementation. Use the mcp Python package (https://github.com/modelcontextprotocol/python-sdk) to export the audit tool as a properly registered MCP tool with transport (HTTP or STDIO).
• Formally define the output schema for audit() in JSON Schema format, documenting each field (audit_results, overall_score, critical_issues, etc.) with types and descriptions. Return this schema in the MCP tool registration.
• Add input validation: reject content_pieces arrays with fewer than 1 or more than 100 items. Return a structured error: { error: 'Invalid content_pieces: must be array of 1-100 strings', retryable: false }
• Document error cases and recovery guidance: 'If audit_results is empty, check that content_pieces is not an empty array. If overall_score is 0, all content pieces violated brand constraints, review critical_issues for specific violations.'
• Add tool annotations to the MCP tool definition: mark audit as readOnlyHint=true (no side effects), non-destructive, and idempotent (same input → same output).
• Add per-piece error handling: if a single content piece is malformed (empty string, non-string), return { index: i, error: 'Empty string or non-string input', issues: [] } instead of silently skipping or crashing.
• Implement rate limiting or a max_tokens parameter to prevent runaway audits on very long documents. Document the limit in the tool description: 'Audit up to 100 content pieces, each up to 10,000 characters.'
• Add an optional dry_run parameter to the tool to support confirmation workflows (future-proofing for pattern:confirmation-request).