This Spring Boot HTTP server defines 3 tools with basic schemas and descriptions, but exhibits significant quality gaps typical of early-stage implementations. Tool naming follows verb_noun convention (brave_web_search, get_current_weather, calculator), which is positive. However, descriptions are minimal (20-60 chars), parameter descriptions are sparse or absent, output schemas are undocumented, and no error handling guidance is provided. The codebase shows tool registration in McpController.java with JSON Schema input definitions, but lacks the LLM-optimized documentation and composition patterns expected of production-grade tools. Per-tool analysis: brave_web_search has a minimal description (57 chars) and a single parameter 'query' with a basic description; calculator has a similarly brief description (103 chars) and three parameters (operation, a, b) with minimal context; get_current_weather has a 93-char description and two parameters with limited detail. None of the tools document their return types, error conditions, or recovery paths. This places the server solidly in the 'fair to poor' range (C-D territory), consistent with community tooling.

• Add a 50 - 150 character description to each tool that answers: What does it do? When should an LLM use it? What is the most common use case? Example: 'brave_web_search: Searches the public web using the Brave Search API. Use this to find current information, news, or answers to factual questions. Returns a ranked list of URLs and snippets.'
• Document the output schema for each tool in the tool definition or in a corresponding README section. Example for calculator: 'Returns: { "result": number, "expression": string, "error": string|null }'
• For calculator's 'operation' parameter, replace the bare description with: 'The mathematical operation to perform. Must be one of: add, subtract, multiply, divide, modulo, power.'
• Add descriptions to undescribed parameters. For calculator's 'a' and 'b', specify: 'First/Second numeric operand. Must be a valid JSON number (integer or float).'
• For get_current_weather's 'format' parameter, change the description to: 'Temperature scale for the response. Must be one of: celsius, fahrenheit. Defaults to celsius.'
• Add an error handling section to the tool documentation (e.g. in a README or OpenAPI description) that explains: What errors can each tool return? How should the LLM respond? Example: 'If brave_web_search returns a 429 error (rate limited), wait 30 seconds and retry. If location is invalid in get_current_weather, try a larger geographic area or ask the user to clarify.'
• Implement per-tool error codes and recovery guidance. When a tool call fails, return a structured error response with: error_code, message, suggestion, and retry_after (if applicable). This enables intelligent agent error recovery.