This repository is a collection of educational AI agent examples, not a cohesive MCP server. Tool definitions are scattered across multiple FastAPI applications with inconsistent schemas and minimal descriptions. Most tools lack parameter type definitions in visible schemas. Descriptions are present but generic (avg ~80 chars, below the 194-char baseline). No input validation, error recovery guidance, or output schema documentation. Security concerns: credentials in environment variables but no explicit secret injection pattern. The 'chat' and 'chat_stream' tools are HTTP endpoints, not MCP-compatible tool definitions. Overall, this reads as prototype code, not production-grade tooling.

• Remove or redesign 'chat' and 'chat_stream' as MCP tools. These are HTTP transport concerns, not tool semantics. If you need a 'send_message' tool, define it with clear input/output schemas and no session management.
• Document output schemas for all tools. For search_web, explicitly define: {results: [{url: string, title: string, snippet: string}], status: 'success'|'error'}. For list_documents, define the document object structure and total count.
• Add confirmation/dry-run for destructive operations. Implement a 'confirm_delete_all_documents' tool that returns a confirmation token, then require that token in the actual delete call. Or add a 'dry_run: bool' parameter.
• Enhance error responses with recovery guidance. Instead of {status: 'error', error: 'Connection timeout'}, return {status: 'error', error: 'Connection timeout', retryable: true, suggestion: 'Retry in 5 seconds or try a shorter URL'}.
• Add parameter constraints and validation. For upload_pdfs, specify: 'files: array of PDF files (max 10 files, max 50MB each)'. For submit_change_request, validate parameter_name against known parameters and return available options if invalid.