The server defines 5 tools with reasonable naming conventions (verb_noun format: get_next_task, mark_task_complete, plan_feature, review_changes, adjust_plan). However, there are significant gaps in parameter descriptions, output schema documentation, and error handling guidance. All tools have descriptions (satisfies the critical requirement), but most are brief and lack context about when to use each tool or what the LLM should expect in responses. Parameter descriptions are present but generic, they state what the parameter is (UUID, string) without explaining the semantic meaning or contextual usage. Output schemas are not documented anywhere in the visible code; the code returns text responses without structured field documentation. Error handling exists but returns unstructured text without recovery guidance. The tools form a logical chain (plan_feature → get_next_task → mark_task_complete → review_changes → adjust_plan), but the composition lacks clear handoff points and required fields for chaining.

• Add output schema documentation to all tools. Define returned fields (e.g., task_id, title, status, effort, parent_task_id for get_next_task; feature_id, created_tasks_count for plan_feature) with types and descriptions.
• Expand parameter descriptions from type annotations to semantic context. E.g., 'feature_id: UUID of the feature to retrieve tasks for. Obtain via plan_feature() response or existing feature database lookup.' Include guidance on what to do if the ID is invalid.
• Restructure error responses to include recovery guidance. E.g., 'No tasks found for feature ID X. Try calling plan_feature() to create tasks, or verify the feature_id with a get_features() lookup call.' Return structured error objects with {message, code, recovery_hint, retryable} fields.
• Document the tool composition chain. Add notes to plan_feature output describing the returned feature_id and how to pass it to get_next_task. Add a note to get_next_task explaining the returned task_id and how to pass it to mark_task_complete.
• Split plan_feature into two tools: analyze_project (scans directory, returns project structure) and generate_feature_plan (takes analysis and description, returns tasks). Enables composition and unit testing.
• Split adjust_plan into two tools: propose_adjustment (analyzes feedback, suggests changes) and apply_adjustment (applies changes to plan). Enables confirmation before destructive edits.
• Add maxLength constraint to feature_description and adjustment_request. Define acceptable length (e.g., 10 - 1000 chars for feature_description, 1 - 500 for adjustment_request).

Inferred effective spec: <=2025-11-25.

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