Critical gaps across all dimensions. Tool definitions lack proper schemas, descriptions are minimal, and parameter documentation is absent. The 'call_tool' tool accepts arbitrary 'arguments' as an untyped object with no validation guidance. 'list_tools' has no input schema visible. Both tools have descriptions under 100 chars that fail to explain WHEN to use them, WHAT they return, or dependencies. No output schemas documented. Error handling is present but generic (McpError with InternalError). No tool annotations (readOnlyHint/destructiveHint). The server acts as a proxy to a Next.js backend, but this architectural detail is not reflected in tool design, tools should expose the actual backend capabilities with proper schemas, not generic wrappers.

• Add explicit input schemas to both tools. For 'list_tools', document any optional parameters (e.g., mcpId filter). For 'call_tool', replace the untyped 'arguments' object with a schema that validates against the backend tool catalog or at minimum documents expected structure.
• Expand descriptions to 150 - 250 chars. Example for 'list_tools': 'Retrieve the catalog of available tools from the Clarity backend. Call this first to discover what operations are available, then use call_tool to execute. Returns tool names, descriptions, and input schemas.' For 'call_tool': 'Execute a tool by name with the provided arguments. Tool names and argument schemas are listed by list_tools. Returns the tool's result or an error with recovery guidance.'
• Document output schemas explicitly. For 'list_tools', show: { tools: [{ name: string, description: string, inputSchema: object }], total: number }. For 'call_tool', show: { success: boolean, result: object, error?: string }.
• Add tool annotations: readOnlyHint=true for 'list_tools'; destructiveHint=true for 'call_tool' (since it can execute write operations). This signals to agents which tools are safe to call speculatively.
• Consider splitting 'call_tool' into domain-specific tools (e.g., 'execute_read_tool', 'execute_write_tool') or expose individual backend tools with proper schemas instead of a generic proxy. This improves LLM reasoning and error recovery.