Single tool 'auto_reply' has a well-structured schema with 4 parameters (to, text, matchedRule, workspace), all with type definitions and descriptions. However, the tool description is policy-focused rather than LLM-optimized, and lacks clarity on when/why to use it. The description mentions 'policy.md' and 'policy rule' without explaining what those are or how the LLM should interact with them. Parameter descriptions are adequate but generic. No output schema is documented. The tool's purpose (autonomous reply with audit logging) is clear, but the description reads more like internal documentation than agent guidance. No error handling guidance provided.

• Rewrite tool description to follow the pattern: 'Sends a Slack message on behalf of the user, subject to rate limiting and audit logging. Use only when a policy rule (from policy.md) or your own judgment has approved the message. Returns: {success: boolean, message_ts: string, audit_log_id: string} or error with retry guidance.'
• Document the output schema explicitly in code and description. Include fields like message_ts (for reference in follow-ups), audit_log_id (for compliance), and rate_limit_remaining (for agent planning).
• Add enum or pattern constraint for 'matchedRule' parameter. E.g., 'Name of the policy rule that approved this reply (e.g., "auto_reply_greeting", "auto_reply_ooo"). Must match a rule in policy.md.'
• Add error handling guidance: 'If rate limit exceeded, wait before retrying. If workspace invalid, check workspace name. If recipient not found, use search_users or search_channels first.'
• Rename tool to 'send_policy_approved_reply' or 'send_auto_reply_message' to start with a clear action verb and improve LLM intent inference.