ContextOverflow demonstrates solid tool design with clear naming (verb_noun pattern), comprehensive descriptions (avg 180 chars), and proper Zod schemas. All 5 tools follow consistent patterns and are well-documented. However, output schemas are not formally documented, responses are text-only without structured field definitions. Parameter descriptions are strong but could benefit from explicit format/constraint documentation. Error handling is minimal; tools return text without recovery guidance. The server is well-composed for its domain (thinking techniques) but lacks the structured output and error classification patterns expected of A-grade tools.

• Document output schemas formally. Define structured responses with typed fields (e.g., categories: [{id, name, description}], techniques: [{id, name, category}]) instead of free-text. This enables LLMs to extract and chain data reliably.
• Add error classification to get_technique and apply_technique. Return {success: false, error: 'not_found', message: '...', suggestion: 'Try find_technique("...") to search by name'} for missing IDs.
• Specify parameter constraints in descriptions. E.g., 'Technique id (kebab-case, 2-50 chars, e.g. "active-listening")' and 'Description (1-500 chars, plain text, no code blocks)'.
• Add optional limit and offset parameters to list_categories and classify_intent. Document default limits (e.g., 'Returns up to 20 matches; use offset for pagination').
• Consider a composite tool for the common path: 'solve_problem(description: string) → {category, techniques: [{id, name, steps, narration, verification}]}' to reduce round-trips for typical agent workflows.

Inferred effective spec: <=2025-11-25.

• Relies on Stateful initialize / Mcp-Session-Id (removed; protocol is stateless) - make each request self-contained