Strong foundation with 8 well-named tools following verb_noun convention (search, read, write, delete, list, put_skill, get_skill, list_skills). All tools have descriptions (avg ~150 chars, within 10-1024 baseline). Input schemas present with type definitions for all parameters. However, output schemas are undocumented, responses are inferred from descriptions rather than formally specified. Parameter descriptions are present but inconsistent in depth; some lack format/constraint details. Error handling guidance is minimal. Tool composition is sound (single responsibility, chainable IDs), but descriptions could be more LLM-optimized per pattern:tool-description.

• Document output schemas formally. For each tool, add a 'Returns' section with JSON Schema (e.g., search returns [{path: string, score: float, snippet: string, type: string}]). This enables LLMs to extract fields reliably.
• Add enum constraints to layer and type parameters in the schema. Replace prose descriptions like 'shared or personal' with explicit enum: ['shared', 'personal']. This prevents hallucinated values.
• Add min/max bounds to numeric parameters. E.g., limit: {type: integer, minimum: 1, maximum: 100, default: 10}. Prevents absurd values like limit=999999.
• Enhance error descriptions with recovery guidance. E.g., read: 'Read a single memory file by store-relative path. Returns {ok, content, type}. If path not found, returns {ok: false, error: "not_found"}. Try list() to discover available paths.'
• Clarify skill validation rules. Document whether write with type='skill' validates the 5 template sections at write time or silently accepts malformed content. If validation occurs, specify the error response format.