Server has 5 tools with basic schemas and descriptions, but significant gaps limit production readiness. All tools have descriptions (10-100 chars, mostly adequate) and input schemas with type definitions. However, parameter descriptions are sparse or missing context. No output schemas documented. Error handling is minimal, tools return JSON-stringified payloads without guidance on failure modes or recovery. No tool annotations (readOnlyHint, destructiveHint, idempotentHint). Tool names follow verb_noun pattern (analyze_*, build_*, validate_*) but lack clarity on when to use one vs. another. Parameters like 'analysis' accept z.unknown() with no validation or format guidance, forcing LLMs to guess structure. No pagination, no per-item error reporting, no chaining IDs in responses.

• Document output schemas for all tools. E.g., analyze_python_module returns {module: {moduleName: string, path: string, classes: [...], functions: [...], imports: [...]}}. Include field descriptions so LLMs know what to extract.
• Replace z.unknown() with explicit Zod schemas for 'analysis' parameters. Define the expected structure (e.g., z.object({moduleName: z.string(), classes: z.array(...), functions: z.array(...)})). Add validation and clear error messages if structure is invalid.
• Add tool annotations: readOnlyHint: true for all tools. This signals to LLMs that these tools are safe to call without confirmation and do not modify state.
• Enhance parameter descriptions with format, constraints, and examples. E.g., 'path: Absolute or relative file path to a .py module. If relative, resolved from current working directory. Returns error if file not found or not valid Python.'
• Implement structured error responses. Instead of returning JSON-stringified payloads, return {success: boolean, data?: {...}, error?: {code: string, message: string, recovery: string}}. Guide LLMs on retry vs. user-fixable errors.