ContextForge has clear naming conventions (verb_noun pattern) and complete input schemas with type definitions and descriptions. However, tool descriptions are minimal (10-30 chars), falling well below the 50-200 char LLM-optimized baseline. Output schemas are undocumented, callers cannot predict response structure. Error handling is absent; no recovery guidance or categorization. Parameter descriptions lack constraints (e.g., max lengths stated in code but not in schema descriptions). No tool annotations (readOnlyHint/destructiveHint) despite clear risk levels. Composition is sound (single responsibility per tool), but missing idempotency guarantees and chaining metadata.

• Expand all tool descriptions to 50-200 chars. Example: 'create_task' → 'Create a new task with a title and description. Status defaults to todo. Returns the created task with id, title, description, status, and created_at timestamp.'
• Document output schemas for all tools. Add a 'Returns' section to each tool docstring specifying field names, types, and meanings. Example: 'Returns: {id: int, title: str, description: str, status: str, created_at: str, completed_at: str|null}'
• Add parameter constraints to schema descriptions. For create_task: 'title (string, required, max 160 chars)', 'description (string, required, max 2000 chars)', 'status (enum: todo|in_progress|done, default: todo)'.
• Implement error handling with actionable messages. Example: 'Invalid task_id: 999 not found. Call list_tasks() to see available tasks.' Categorize errors as retryable (database timeout) vs user-fixable (invalid status).
• Add tool annotations to server.py tool registration. Mark delete_task with destructiveHint=true, list_tasks with readOnlyHint=true, and create_task with idempotentHint=false to guide agent retry logic.