Single tool 'execute_skill_action' has a vague, marketing-focused description that does not explain what the tool actually does or when to use it. The input schema is minimal (single string parameter 'query_payload') with no type constraints, enums, or validation guidance. No output schema is documented, the tool returns a generic JSON echo with no structured fields the LLM can rely on. The tool name uses 'execute' + 'action', which is generic and does not convey the actual operation (markdown chunking, document parsing, etc.). No error handling, no parameter constraints, no recovery guidance. The implementation is a stub that echoes input rather than performing real work.

• Rename tool to a specific verb_noun pair: 'chunk_markdown_document', 'parse_document_tables', or 'extract_document_sections', something that tells the LLM what action occurs.
• Replace marketing description with a functional spec: 'Splits a Markdown document into RAG-optimal chunks while preserving heading hierarchy (H1 > H2 > H3) and metadata anchors. Returns structured chunks with breadcrumbs, token estimates, and content. Use this to prepare long documents for retrieval-augmented generation.'
• Add parameter constraints to 'query_payload': specify expected format (JSON object with 'markdown_text' and optional 'max_chunk_tokens'?), provide a schema example, and document valid ranges (e.g., max_chunk_tokens: 100 - 2000).
• Document the output schema explicitly: array of chunk objects, each with chunk_id, heading_breadcrumbs (array), context_header (string), content (string), raw_text (string), estimated_tokens (number). Include example output.
• Integrate the HierarchicalMarkdownChunkerHeadingAnchor class into the MCP server's tools/call handler so the tool actually performs markdown chunking instead of echoing input.