Critical failure: Tool definitions are inferred from a static JSON manifest (public/.well-known/mcp.json) with NO visible schema, parameter types, or detailed descriptions in the provided source. All 64 tools show only name and brief description (10-50 chars). No input schemas, output schemas, parameter types, or error handling guidance are visible. The server appears to be HTTP-based but tool registration is opaque. Average across 64 tools with minimal metadata yields a failing grade.

• Expand every tool description to 100-200 characters. Include: (1) what the tool does, (2) when to call it vs similar tools, (3) what it returns. Example: 'Search across all sources and personal memory for topics matching a query. Returns ranked results with source citations. Use this before fetch to discover relevant content.'
• Define explicit JSON Schema for every tool's input parameters. Include type, description, enum constraints, min/max bounds, and format patterns. Example: { 'query': { 'type': 'string', 'description': 'Search term (1-500 chars)', 'minLength': 1, 'maxLength': 500 } }
• Document output schemas for all tools. Specify field names, types, and what each field represents. Example: { 'results': [{ 'id': 'string', 'title': 'string', 'source': 'string', 'relevance_score': 'number' }], 'total_count': 'number' }
• Add error handling guidance to descriptions. State what can go wrong and what the agent should do. Example: 'If no results found, try a broader query or use find_missions() to discover available topics.'
• Rename ambiguous tools: 'search' → 'search_sources_and_memory', 'fetch' → 'fetch_source_content', 'prepare_context' → 'prepare_reasoning_context_from_sources'. Verb_noun naming with specificity prevents LLM confusion.