This MCP server has severe definition quality issues across nearly all dimensions. Tool descriptions are minimal or absent (most under 50 chars). Input schemas are present but parameters lack detailed descriptions. No output schemas are documented. Error handling provides no recovery guidance. The server implements a task queue workflow with 7 tools, but the definitions are underspecified for reliable LLM use. Only 3 of 7 tools have descriptions exceeding 20 characters. Parameter descriptions are typically one-line placeholders. No tool distinguishes between retryable vs. fatal errors or provides actionable error messages.
Tools (7)
add_taskwritesource verified57/100
Add a new task to the queue
clear_completeddestructivesource verified45/100
Remove all completed tasks from the queue
complete_taskwritesource verified63/100
Mark a task as successfully completed
fail_taskwritesource verified62/100
Mark a task as failed
get_next_taskread onlysource verified53/100
Get the next pending task from the queue
reset_taskwritesource verified50/100
Reset a failed task back to pending for retry
start_taskwritesource verified53/100
Mark a task as in_progress and increment attempt counter
Most tool descriptions are under 50 characters and lack WHEN to use them. Examples: 'Mark a task as in_progress and increment attempt counter' (58 chars), 'Mark a task as successfully completed' (38 chars). These fail the completeness bar.
No output schemas are documented for any tool. The source shows input schemas for add_task, start_task, reset_task, get_next_task, clear_completed, but no documentation of what these tools return. LLMs cannot plan chained calls without knowing return types. Complete_task and fail_task are defined in claude_tools.py with input schemas but no return type documentation visible.
Expand every tool description to 50 - 200 characters. State WHAT it does, WHEN to call it, and any prerequisites. Example: 'Get the next task from the queue. Call this first to retrieve a pending task before attempting it. Returns task ID, description, and attempt count. If no pending tasks exist, returns empty.'
Document the output schema for every tool. Define what fields are returned, their types, and what they mean. For get_next_task, specify: {task_id: string, description: string, attempt_count: number, max_attempts: number}.
Expand parameter descriptions to explain format and constraints. Example: task_id → 'Unique identifier of the task (UUID string). Find available task IDs by calling get_next_task().' For description in add_task → 'What needs to be done (max 1000 characters). Include any constraints or acceptance criteria.'
Add error handling guidance to each tool. Example for start_task: 'Error: task_id not found or already in_progress. Ensure the task_id is valid (call get_next_task() to list available tasks). If the task is already started, reset_task() first if retryable.'
Clarify fail_task behavior: 'When retryable is true and attempts remain, the task resets to pending and can be retried. When retryable is false or max attempts reached, the task remains failed and is moved to a failed queue.' Update the description to include this.
Add a dry-run or confirmation step for clear_completed. Document the consequence: 'This irreversibly removes all completed tasks. Consider asking the user to confirm: "Delete all X completed tasks?" before calling this tool.'
Parameters lack detailed descriptions. Examples: task_id is described as 'ID of the task to start' (no format, no constraints, no hint about opaque IDs vs human-readable names). 'description' in add_task is 'What needs to be done' (18 chars, no length limits, no format guidance).
No error handling or recovery guidance. The tools declare 'fail_task' but do not document what error responses look like, when errors are retryable, or what the LLM should do next. Use get_next_task() to list available tasks.' This server provides none of that.
Parameter naming is inconsistent and underspecified. 'start_task' accepts 'task_id', 'reset_task' accepts 'task_id', but 'complete_task' and 'fail_task' (in claude_tools.py) use no explicit task ID parameter in their visible input schema, it's unclear how they identify the current task.
Tool composition: 'fail_task' accepts a 'retryable' boolean with a default of true, but the description does not explain what 'retryable' means in the context of the task queue (e.g., does true reset to pending? does it increment attempts?). Dependencies between parameters are undocumented per pattern:constrained-input.
fail_task
For complete_task and fail_task in claude_tools.py, clarify the task identification mechanism. If these are called within an active task context, document that explicitly: 'Called within a task execution context. Marks the current task as complete/failed based on the active task ID maintained by the Ralph Loop.'
Add per-parameter constraints where applicable. For confidence in complete_task, ensure the schema bounds (0.0 - 1.0) are documented in the description. For error message strings, specify max length (e.g., 500 chars).
Implement error classification. Distinguish between: retryable errors (e.g., 'Task temporarily locked'), user-fixable errors (e.g., 'Invalid task_id'), and fatal errors (e.g., 'Task queue corrupted'). Return error responses that include a classification and suggested next steps.
Document tool chaining. After get_next_task returns a task, clarify which tool to call next (start_task, or directly attempt and call complete_task or fail_task). This guides LLM planning.