ccRecall demonstrates solid tool design with complete input schemas, clear descriptions, and thoughtful parameter documentation. All three tools (recall_query, recall_save, recall_context) have well-structured Zod schemas with type constraints and detailed descriptions. The recall_save tool shows sophisticated design with enum-constrained memory types, confidence scoring, and origin tracking. However, output schemas are not explicitly documented in the visible code, responses are formatted as text results rather than structured objects. Error handling returns text-wrapped errors but lacks recovery guidance or error classification. Tool names follow verb_noun convention appropriately. Parameter descriptions are comprehensive (averaging 150+ chars), exceeding the 72-char baseline, with explicit constraints on projectId format and token budgets.

• Document output schemas explicitly. Define McpTextResult as a structured type with typed fields (e.g., memories: Array<{id, type, content, confidence}>) so LLMs can extract and chain results.
• Enhance error responses with recovery guidance. Instead of 'Error querying memories: ...', return 'Query failed: invalid FTS5 syntax. Try simpler keywords or check projectId format (alphanumeric + dashes only).'
• Add cardinality and ordering guidance to recall_context.keywords. E.g., 'Array of 1-5 topic keywords; results grouped by keyword relevance; order does not affect ranking.'
• Implement pagination for recall_context. Return a next_cursor or indicate when results are truncated, allowing agents to fetch additional memories without re-querying.
• Simplify recall_save.origin documentation. E.g., 'Set to "agent-inferred" only when saving from automated transcript analysis; leave unset (defaults to "explicit") for interactive saves.'