Single tool with minimal schema and description. Tool description is 79 chars, below the 194-char baseline and lacks WHEN/WHY context. No output schema documented. No error handling guidance. No parameter constraints (enums, ranges, formats). The tool performs a stateful operation (pruning) but description does not indicate this is a read-only analysis. Missing actionable recovery guidance for edge cases.

• Add detailed description to 'turns' parameter: 'Array of conversation turn objects. Each turn must have: role (string: "user" or "assistant") and content (string: the message text). Example: [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there"}]'
• Document output schema in tool description or as a separate schema field. Specify that response contains: initial_turn_count (int), retained_turn_count (int), dropped_turn_count (int), retained_turns (array of turn objects with entropy field), dropped_turns (array of turn objects with entropy field).
• Expand tool description to 150-250 chars explaining: (1) What it does: removes low-information turns from conversation history. (2) When to use: before sending long context to LLM to reduce token cost and noise. (3) What it returns: pruned conversation with entropy scores and statistics.
• Expose min_entropy_threshold as an optional tool parameter (default 2.0, range 0.0-8.0) so LLMs can adjust pruning sensitivity without server restart.
• Add error handling: if all turns are dropped, return a warning in the response: 'All turns were dropped. Consider lowering min_entropy_threshold or reviewing turn content quality.' Return partial results rather than failing completely.