Server exposes 26 tools (13 unique operations duplicated across MCP and LangChain interfaces). Tool naming follows verb_noun convention (list_agents, create_agent, get_call_transcript). All tools have descriptions (10-60 chars, mostly adequate). Input schemas are present and properly typed with Pydantic BaseModel definitions. However, output schemas are undocumented, tools return JSON strings with no declared structure. Parameter descriptions are present but minimal (avg 40-60 chars). No error handling guidance, no recovery hints, no validation constraints (e.g., enums for language codes, model names, sort_order). Destructive operations (delete_agent) lack confirmation patterns. Duplicate tool registration (MCP + LangChain) creates maintenance burden and potential LLM confusion.

• Document output schemas for all tools. Define the structure of returned JSON (e.g., Agent object with fields: agent_id, agent_name, voice_id, prompt, language, model, responsiveness, interruption_sensitivity, enable_backchannel, created_at). Use JSON Schema or TypedDict.
• Consolidate duplicate tools. Remove langchain_tools.py duplicates or make it a thin wrapper that imports from server.py. Maintain a single source of truth.
• Add enum constraints for constrained parameters: language (en-US, es-ES, fr-FR, etc.), model (gpt-4o-mini, gpt-4o, claude-3-5-sonnet), sort_order (ascending, descending). Declare in schema and description.
• Implement error recovery guidance. When a tool fails, return actionable hints: 'Agent not found. Call list_agents() to see available agents.' or 'Invalid phone number format. Use E.164: +1234567890.'
• Add confirmation pattern for delete_agent. Require a second parameter confirm=true or return a dry-run result first. Prevent accidental permanent deletion.