AIDOCS MCP provides 10 tools with explicit schemas and descriptions. Naming follows verb_noun convention (verify_*, ai_*). Descriptions are detailed (100-400 chars) and explain use cases well. However, critical gaps exist: (1) Input schemas lack enum constraints for constrained parameters (e.g., ai_events 'action_kind', 'event_kind' are free-form strings); (2) Output schemas are entirely undocumented, no return type specifications visible in tool definitions; (3) Error handling descriptions are absent, tools do not explain failure modes or recovery paths; (4) Parameter descriptions lack format/range constraints (e.g., 'limit' accepts 1-500 but this is buried in prose, not formalized); (5) Batch tools (ai_batch_str_replace, ai_batch_edit) lack per-item error reporting structure. Edit tools are well-composed (separate concerns: str_replace, anchor_replace, edit_lines, insert_lines, create, replace) but lack idempotency guarantees or dry-run support for destructive operations.

• Document output schemas for all 10 tools. Specify return type (object/array), field names, types, and whether fields are always present or conditional. Example: verify_audit_chain returns {verified: boolean, first_broken_link?: {event_id: string, expected_hash: string, stored_hash: string}}.
• Add enum constraints to JSON Schema for ai_events parameters. Replace free-form 'action_kind' and 'event_kind' strings with explicit enums: action_kind: [tool_call_started, tool_call_completed, ...], event_kind: [audit, log, ...]. Update descriptions to reference the enum.
• Implement dry-run or confirmation pattern for destructive edit tools. Add optional 'dry_run: boolean' parameter to ai_str_replace, ai_replace, ai_batch_* tools. When true, return the proposed changes without applying them. Document idempotency: 'Calling ai_str_replace twice with identical parameters is safe, the second call is a no-op if the replacement already exists.'
• Formalize parameter constraints in JSON Schema. Add minimum/maximum to 'limit' (1-500), add pattern or description to 'path' (e.g., 'relative path within project root; no ../ traversal'), add minLength/maxLength to string parameters. Move constraints from prose to schema so LLMs and validators can enforce them.
• Document batch operation error handling. Specify whether ai_batch_str_replace returns {results: [{path, status, error?}]} with per-item success/failure, or fails entirely on first error. If partial success is possible, explain rollback behavior and whether the LLM should retry individual items or the entire batch.