Critical failures across all dimensions. The server dynamically generates tool names and descriptions at runtime from discovered connectors, making static schema validation impossible. Tool definitions are inferred from connector metadata rather than explicitly registered with proper JSON Schema. No input schemas visible in source code. Descriptions are templated strings like '[{connector_name}] {tool_description}' with no validation of actual content. No parameter documentation, no output schema definition, no error handling guidance. The tool registration mechanism (lines in server.ts) shows tools are created dynamically but without the required schema structure for LLM reasoning.

• Implement explicit tool registration with full JSON Schema for each discovered connector tool. Fetch and validate connector tool schemas from the proxy, then re-register them with descriptions and parameter documentation.
• Add parameter documentation layer: for each proxied tool, document expected parameters, their types, constraints, and examples in the tool description or a separate metadata structure.
• Validate and normalize tool descriptions from connectors to meet 10-1024 character requirement and include WHAT the tool does, WHEN to use it, and any prerequisites.
• Implement output schema documentation: capture the response structure from each connector tool and document it for LLM planning (e.g., 'Returns object with fields: id, name, status, created_at').
• Add error handling guidance: wrap proxy calls with try-catch, categorize errors as retryable/user-fixable/fatal, and return actionable messages like 'User not found. Try search_users() first.' instead of raw proxy errors.