Telegram-mcp has 4 tools with explicit schemas and descriptions, but significant quality gaps limit production readiness. Tool naming is verb-based (telegram_*), which is good, but descriptions lack actionable context for LLM selection. telegram_execute_code is particularly risky, it accepts arbitrary Python with no input validation, timeout enforcement, or sandbox isolation. Parameters are typed but many lack constraints (enums, ranges). Error handling is minimal; most tools return raw exceptions rather than recovery guidance. No tool annotations (readOnlyHint/destructiveHint) despite clear risk profiles. Output schemas are undocumented, LLMs cannot predict response structure for chaining. The server provides no pagination, rate-limit guidance, or idempotency guarantees.

• Add tool annotations: mark telegram_status as readOnlyHint=true; mark telegram_execute_code, telegram_send_command, telegram_send_message as destructiveHint=true. This signals to LLMs which tools have side effects.
• Document output schemas for all tools. E.g., telegram_send_message should return {message_id: int, timestamp: string, status: string, chat_id: int}. This enables LLM chaining and reduces guessing.
• Add enum constraints: parse_mode=['md', 'html'], timeout_seconds min=1 max=300. Validate bot_username format (alphanumeric + underscore, 5-32 chars). Return actionable errors: 'Invalid parse_mode: got "xml", must be md or html'.
• Implement input validation and sanitization for telegram_execute_code. Reject code containing dangerous patterns (os.system, subprocess, __import__). Add a dry_run parameter to preview execution without side effects. Consider a code sandbox or allowlist of safe APIs.
• Standardize error responses across all tools. Return {error: string, error_code: string, recovery_hint: string, retryable: boolean}. E.g., 'error: "User not found", recovery_hint: "Call telegram_status to verify connection", retryable: false'.