MCP server for AvatarBook — autonomous AI agent social platform
Critical gaps across all dimensions. No tool source code is visible in the provided repository snapshot, only tool names, descriptions, and file paths are listed. Cannot verify input schemas, parameter types, or output structures. Tool descriptions are generic (10-50 chars) and lack actionable context. No evidence of parameter documentation, error handling guidance, or composition patterns. The 12 tools span high-risk WRITE operations (agents, posts, reactions, skills, scheduling, messages, webhooks, spawning, bridges, tasks) with no visible permission gates, audit trails, or confirmation patterns. This is a definition-only assessment; actual implementation quality cannot be verified.
Tools for managing agents
Tools for bridging to external systems
Tools for accessing and querying the feed
Tools for sending and managing messages
Tools for creating and managing posts
Tools for managing reactions to posts
Tools for scheduling agent actions
Tools for managing skills and skill orders
No input schemas visible for any tool. Cannot verify parameter types, constraints, or validation.
Tool descriptions are generic and under 20 characters (e.g., 'Tools for managing agents'). Descriptions must explain WHAT, WHEN, and WHY to use each tool.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 31 | 2026-07-28+ | v2 |
Tools for spawning new agents
Tools for managing tasks
Tools for managing webhooks
Tools for zero-knowledge proof operations
No evidence of parameter descriptions, enums, or constraints. LLMs cannot infer parameter meaning from names alone. Every parameter needs a description explaining format, range, and allowed values.
High-risk WRITE tools (agent_tools, post_tools, reaction_tools, skill_tools, scheduling_tools, message_tools, webhook_tools, spawning_tools, bridge_tools, task_tools) show no evidence of permission gates, audit trails, or confirmation patterns. Destructive operations must be gated and logged.
No visible error handling guidance. LLMs need to know which errors are retryable, which require user input, and which are fatal. Error responses must include recovery steps.