Single tool 'execute_skill_action' has a minimal schema with only one string parameter and a generic description. The tool name does not follow verb_noun convention (should be 'execute_skill' or similar). Description (118 chars) lacks specificity about what 'deterministic, zero-dependency AI Document Intelligence & Vision-Table Parsing operations' actually means, no guidance on when to use it, what it returns, or how to structure the query_payload. The input schema has a type and description for query_payload, but lacks examples, constraints, or format guidance. No output schema is documented. The tool appears to be a pass-through echo service rather than a functional OCR regularizer despite the client.py containing a working OCRConfidenceScoreSpellcheckRegularizer class that is never integrated into the MCP server.

• Rename tool to 'regularize_ocr_tokens' and update description to: 'Repair OCR transcription artifacts in token lists. Fixes common glyph confusions (rn→m, 0↔O, 1↔l), ligatures (fi→fi), and diacritics. Input: JSON array of {text, confidence} objects. Output: {total_tokens, corrections_applied, repaired_string, tokens[]}.'
• Add input schema with explicit structure: query_payload should be a JSON string containing {tokens: [{text: string, confidence: number}], min_confidence_threshold?: number}. Document the expected format in the parameter description.
• Document the output schema in the tool definition: {type: object, properties: {total_tokens: number, corrections_applied: number, repaired_string: string, tokens: array}}.
• Integrate the OCRConfidenceScoreSpellcheckRegularizer class from client.py into the tools/call handler so the tool actually performs regularization instead of echoing input.
• Add error handling: return actionable errors like 'Invalid query_payload: expected JSON with {tokens: [{text, confidence}]}' instead of generic exceptions.