ContextBolt MCP has well-structured tool definitions with consistent naming (verb_noun pattern) and complete input schemas. All 5 tools have descriptions and typed parameters. However, descriptions are brief (avg ~80 chars, below the 194-char baseline), lack actionable context for LLM selection, and output schemas are entirely undocumented. Parameter descriptions are minimal, many lack format/constraint guidance. No error handling guidance, no pagination support for list tools, and no indication of which tools modify state vs. read-only (though risk labels suggest awareness). The server proxies to a backend API, so actual validation and error recovery happen server-side, but the MCP interface itself lacks the detail needed for confident LLM tool selection and error recovery.

• Document output schemas for all tools. E.g., search_bookmarks returns [{id, url, title, author, platform, saved_at, tags}]. Include field types and whether arrays are paginated.
• Expand descriptions to 100 - 200 chars. For list_clusters: 'List all your topic clusters with bookmark counts. Call this first to explore available topics before searching or fetching cluster details.' For get_recent_bookmarks: 'Retrieve your most recently saved bookmarks, optionally filtered by platform (twitter, reddit, linkedin, web). Useful for quick context or recent activity.'
• Add enum constraints to 'platform' parameter: {type: 'string', enum: ['twitter', 'reddit', 'linkedin', 'web'], description: 'Source filter. If omitted, searches all platforms.'}
• Add pagination to list_clusters and get_cluster_bookmarks: include 'offset' or 'cursor' parameter, and return 'total_count' and 'has_more' in response. Cap default limit at 20 - 50 items.
• Add error guidance to save_bookmark description: 'Saves a bookmark to your collection. Requires valid URL and content. Returns bookmark_id on success. If save fails, check that URL is accessible and content is not empty.'