AbhiMeet MCP server has 6 tools with basic schemas and descriptions, but significant gaps limit production readiness. All tools have descriptions (10-200+ chars) and input schemas with type definitions, meeting baseline requirements. However, descriptions lack LLM-optimization guidance (e.g., 'when to use', 'prerequisites'), parameter descriptions are minimal, and output schemas are undocumented. No error handling guidance, no pagination support for list_recordings despite potentially large result sets, and no tool annotations (readOnlyHint/destructiveHint). Tool naming is clear and verb-based (list_, get_, transcribe_), but composition could be improved, transcribe_recording bundles model selection and language hints that could be separate concerns. Security concern: transcribe_recording accepts lang_hint as free-form string without enum constraint, inviting invalid values.

• Document output schemas for all tools. E.g., list_recordings should return {recordings: [{id, title, created_at, status, duration_seconds}], total_count, has_more}. This enables agents to extract and chain IDs.
• Add pagination to list_recordings: include offset/limit parameters with defaults (limit=20, offset=0), return total_count, and document max result size in description.
• Convert transcribe_recording lang_hint to enum: {type: 'string', enum: ['auto', 'hi', 'gu', 'en', 'hi-gu', 'hi-en', 'hi-gu-en', 'mr', 'bn']}. Remove free-form string acceptance.
• Add error recovery guidance to all tool descriptions. E.g., get_recording_info: 'If recording_id is invalid, call list_recordings() first to find the correct ID.'
• Add tool annotations via MCP schema: mark list_recordings with readOnlyHint=true, transcribe_recording with destructiveHint=true. This signals side effects to agents.