AgentiBridge demonstrates solid fundamentals with 17 well-named tools following verb_noun conventions. All tools have descriptions (194 - 350 chars, within baseline range). Input schemas are present and mostly complete. However, critical gaps emerge: (1) Output schemas are NOT documented in visible code, LLMs cannot infer what fields to expect from responses; (2) Error handling is absent, no guidance for recovery, no categorization of retryable vs fatal errors; (3) Tool 'dispatch_task' is the only write tool but lacks confirmation/dry-run pattern; (4) Some parameters lack constraints (e.g., timeout unbounded, threshold 0-1 not validated). Naming is strong (list_, get_, search_, dispatch_), but composition could be tighter, tools are functional but design does not yet follow 54 patterns rigorously.

• Document output schemas for all tools. For example: 'list_sessions returns {sessions: [{session_id, project, created_at, summary, message_count}], total: int}'. LLMs need to know what fields are available for downstream decisions.
• Add error handling guidance: Create a companion 'Error Reference' doc listing common failure modes (SessionNotFound → suggest search_sessions; SearchTimeOut → suggest retry with smaller limit) for each tool.
• Implement confirm_before_execute pattern for dispatch_task: add optional 'dry_run' bool parameter (default=false) to preview what would execute without side effects.
• Add JSON Schema constraints to all numeric parameters: limit (min=1, max=100), offset (min=0), timeout (min=1, max=3600), threshold (minimum=0, maximum=1). These enforce validity at schema validation time, reducing invalid LLM calls.
• Enhance pagination: ensure all list/search responses include 'total_count' field and document the max_limit (e.g., 'limit capped at 100; use offset for pagination'). This enables LLMs to reason about pagination logic.
• Standardize response IDs: Create a naming convention doc (session_id, project_id, job_id, plan_codename) and audit all tools to ensure consistent key names in responses for tool chaining.
• Add 'When to use' guidance to tool descriptions: e.g., 'Use search_semantic when keyword search (search_sessions) returns too many results; it ranks by relevance.' Helps LLM pick the right tool first.
• Validate secret injection: Ensure model selection (dispatch_task.model) and OAuth credentials (server config) are never exposed in responses or logs. Verify via code review of dispatch_task implementation.

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

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