Single tool 'execute_skill_action' has critical definition gaps. Description is vague (67 chars, below 10-char minimum for clarity) and does not explain WHAT the tool does, WHEN to use it, or what it returns. Input schema exists but parameter 'query_payload' lacks any meaningful description, just 'Input execution payload' which is circular and uninformative. No output schema documented. Tool name does not follow verb_noun convention (should be 'execute_action' or similar). No error handling guidance. No indication of what 'deterministic, zero-dependency AI Agent Operations' means in practice. The implementation is a stub that always returns success, making it impossible to assess real behavior.

• Rename tool to a specific action verb: 'extract_table_from_image' or 'analyze_document_structure' to match the server's stated purpose and follow verb_noun convention.
• Rewrite tool description to answer: (1) What does it do? (2) When should the LLM call it? (3) What does it return? Example: 'Extract table structure from an image. Call this to parse tables, detect merged cells, and return markdown-formatted results. Returns extraction_id, row/column counts, and structured table data.'
• Add detailed parameter description for 'query_payload': specify expected format (JSON object? image URI? base64?), required fields, and constraints. Example: 'JSON object with image_uri (string, required) and expected_columns (array of strings, optional). Image must be PNG or JPEG under 10MB.'
• Document output schema explicitly in the tool description or as a separate schema field. Include all returned fields: extraction_id, detected_table_count, reconstructed_rows_count, reconstructed_columns_count, merged_cells_resolved_count, bounding_box_coverage, clean_markdown_table, structured_dossier_url.
• Add error handling guidance: document what errors can occur (invalid image format, timeout, no table detected) and what the LLM should do (retry, ask user for different image, etc.).