SOPR MCP implements 8 tools with full Zod schema validation and documented input constraints. Naming follows verb-noun convention (snap, check, learn, integrate, pulse, graph, cache, help). All tools have descriptions and parameter documentation. However, tool descriptions are brief (19-118 chars), most lack output schema documentation, and error handling guidance is not visible in the source. The server prioritizes input validation (SafePathSchema, bounds checking, enums) over LLM-centric output design and recovery patterns. Strengths: comprehensive input schemas with security bounds; clear mode discriminators; safe path validation. Weaknesses: minimal output documentation; descriptions lack 'when to use' context; no visible error recovery guidance; tool compositions and chaining unclear.

• Add full output schema documentation for each tool. Include return type definition, field descriptions, and example response structure. This enables LLMs to plan tool chaining.
• Expand tool descriptions to 50-200 chars following the pattern: [What] + [When/Context] + [Prerequisites]. E.g., 'Start a snapshot to establish task context. Modes: start (begin task + capture intent), check (validate task scope), context (retrieve task metadata), end (finalize), undo (revert + mask). Use when beginning a developer task or transitioning between modes.'
• Implement structured error responses with actionable recovery hints. E.g., 'Path traversal detected in file parameter. Use relative paths within workspace (e.g., "src/index.ts", not "../secrets.env"). Or call help?mode=patterns to see valid file handling.',
• Decompose multi-mode tools. Instead of snap with 5 modes, consider: snap_start, snap_check, snap_context, snap_end, snap_undo. This matches the verb_noun pattern and clarifies single responsibilities.
• Define the 'cache.value' type constraint explicitly. E.g., 'value: object | string | number | array, JSON-serialized size ≤ 1MB. For error cache: {error_code: string, message: string, timestamp: number}. For pattern cache: {pattern_name: string, matches: string[], context: string}.'
• Add batch operations where agents loop: e.g., learn_batch(mode, learnings: [{trigger, action, type}, ...]) to capture multiple learnings in one call.
• Document tool chaining: which tools produce output needed by downstream tools. E.g., 'snap?mode=start returns task_id; pass to check?mode=full for validation; result includes pattern_ids for learn?mode=save.'

Inferred effective spec: 2025-06-18+.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry