AgentLoop provides four tools with explicit JSON Schema definitions and annotations. Tool naming follows verb_noun convention (agentloop_status, dispatch_task, start_loop, send_message), which is good. However, descriptions are terse (range: 34-76 chars), falling below the production baseline of 194 chars average. More critically, NO parameter descriptions are provided, the inputSchema objects define constraints (enums, min/max, required fields) but lack description fields on individual parameters. This forces LLMs to infer meaning from parameter names alone, violating pattern:tool-description. Structured output is present via structuredContent in toolResult(), and tool annotations (readOnlyHint) are implemented correctly. Error handling returns actionable messages via daemonError() and toolFailure(), which is good. The composition is sound, four distinct single-purpose tools with clear responsibilities.

• Add a 'description' field to every parameter in each inputSchema. Example for dispatch_task.prompt: 'A detailed description of the task to execute. Will be sent to the language engine (claude or codex) for planning and execution.'
• Expand tool descriptions to 100-200 characters. Example for agentloop_status: 'Retrieves the current state of AgentLoop, including daemon status, pending/running/completed task counts, and recent task results. Call this to monitor loop progress or check if the daemon is responsive.'
• Remove 'codex' from the engine enum or clarify its status. If OpenAI Codex is no longer supported, replace with a current model name and document the change in release notes.
• Document the output schema for each tool. Example for dispatch_task: 'Returns {id: string} on success, where id is the task UUID. Use this id to track task progress via agentloop_status.'
• Add dependency hints to descriptions. Example for dispatch_task: 'Call agentloop_status first to verify the daemon is running before dispatching tasks.'
• Document parameter relationships in start_loop: explain that maxCycles bounds the number of improvement iterations, taskRetries controls failure tolerance per task, and polish controls whether optimization continues after the initial plan succeeds.

Inferred effective spec: 2025-06-18+.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry