This MCP server has 5 tools with reasonable naming (verb-noun convention) and descriptions present, but significant gaps in schema completeness, parameter documentation, and output specification. The `chat` tool has the most detailed schema definition visible in the code, with proper parameter types and descriptions (messages, model, provider, temperature, max_tokens, stream). However, the remaining 4 tools (list_models, compare, analyze, generate) show incomplete schema definitions, parameter types are not visible in the source code provided, only inferred from context. The server returns error responses as dictionaries without structured guidance, and lacks pagination support for list operations. No error classification, retry guidance, or recovery patterns are evident. The descriptions are adequate (50-150 chars) but lack depth around when to use each tool vs. similar ones, and no dependency hints are provided. The output schema for tools is mentioned in docstrings but not formally defined in a way that's parseable by clients.

• Add formal JSON Schema input definitions for list_models, compare, analyze, and generate tools. Each parameter must have explicit type (string, integer, float, boolean, array), description, and constraints (enum, min/max, pattern) visible in the schema, not just docstrings.
• Document output schemas for all 5 tools. Define the exact fields returned, their types, and semantics. E.g., chat returns {content: str, model: str, provider: str, usage?: {prompt_tokens: int, completion_tokens: int, total_tokens: int}}. This enables LLMs to plan follow-up actions.
• Implement structured error responses with actionable recovery guidance. Instead of {'error': str(e)}, return {'error': str(e), 'error_type': 'retryable|user_fixable|fatal', 'suggestion': 'Try X', 'available_options': [...]}. Example: if model not found, suggest list_models().
• Add pagination to list_models. Accept optional limit (default 20, max 100) and offset (default 0) or cursor parameters. Return total count and next_cursor in response for continuation.
• Make analyze tool accept optional custom_prompt parameter to override hard-coded analysis prompts. This allows agents to specify domain-specific or multi-step analyses without requiring a new tool.
• Standardize chat output schema. Document whether streaming returns a string or structured chunks. When stream=True, consider returning {content: str, chunks: int, model: str, ...} so the LLM knows streaming occurred.
• Add tool annotations (readOnlyHint, destructiveHint) to clarify side effects. All 5 tools are marked READ_ONLY in the spec, ensure tool definitions include readOnlyHint=true so clients understand these don't mutate state.