Single tool 'execute_skill_action' has critical definition gaps. Description is vague (79 chars, below 100-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 actionable description, it is a free-form string with no format, length, or content constraints. No output schema is documented; the tool returns a generic JSON success message with no structured fields. No error handling guidance. Tool name uses 'execute' (generic verb) rather than domain-specific action like 'remove_filler_words' or 'clean_speech_transcript'. The implementation is a stub that always returns success, making it unsuitable for production use.

• Rename tool to 'remove_filler_words_from_transcript' or 'clean_speech_transcript' to reflect the actual domain action and help LLMs infer intent from the name.
• Replace 'query_payload' with explicit, typed parameters: 'transcript_segments' (array of {word, start_sec, end_sec}), 'filler_word_list' (array of strings, optional), 'output_format' (enum: json|cutlist_url). Add descriptions for each.
• Document the output schema with structured fields: {cleaner_run_id, total_words_evaluated, fillers_detected_count, total_filler_duration_sec, flagged_words, clean_cutlist_export_url}. Match the client.py response structure.
• Rewrite tool description to: 'Detects and removes filler words (um, uh, like, you know, ah) from speech transcripts. Returns a list of flagged words with timestamps and a cutlist export URL for audio editing. Call this to clean up recorded interviews, podcasts, or presentations.'
• Add error handling: document what can fail (invalid transcript format, unsupported audio codec), return error codes with recovery hints (e.g., 'Invalid transcript format. Expected array of {word, start_sec, end_sec} objects.').