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 actionable description, it is a free-form string with no format guidance, constraints, or examples of valid payloads. No output schema is documented; the tool returns a generic JSON success message with no structured fields for the agent to extract or chain. Tool name uses 'execute' (generic verb) rather than a specific action like 'remove_filler_words' or 'analyze_speech'. No error handling guidance, no pagination, no idempotency markers. Naming does not reflect the actual domain (speech filler removal), 'execute_skill_action' could apply to any skill, making it indistinguishable from other generic tools.

• Rename tool to 'remove_filler_words' or 'analyze_speech_fillers' to reflect the actual domain action and help LLMs infer intent from the name alone.
• Replace 'query_payload' with domain-specific parameters: 'transcript' (string, required), 'filler_words' (array of strings, optional, default=['um','uh','like']), 'output_format' (enum: 'json'|'cutlist'|'srt'). Add descriptions for each.
• Document the output schema with typed fields: 'fillers_detected' (array of {word, start_sec, end_sec, duration_sec}), 'total_duration_sec' (number), 'cutlist_url' (string), 'status' (enum: 'success'|'error').
• Expand tool description to 100-150 chars: 'Detect and remove filler words (um, uh, like, etc.) from speech transcripts. Returns timestamps and duration saved. Use this to clean audio for editing or analysis.'
• Add error handling: return structured errors with actionable guidance, e.g. 'Invalid transcript format. Expected array of {word, start_sec, end_sec}. Got: <actual>. Retry with corrected format.'