Loading ToolBench
Enterprise-grade memory management for AI agents with multi-tier memory engine and semantic search capabilities
Critical failures across naming, descriptions, and schemas. The server defines 8 tools but exhibits severe duplication: 'remember', 'recall', 'context', and 'forget' each appear twice with nearly identical schemas and descriptions. Descriptions are marketing-heavy ('enterprise-grade', 'hyper-fast') rather than functional. Parameter descriptions are minimal (1-3 words). No output schemas documented. No error handling guidance. Schemas present but lack depth (no constraints, enums, or validation rules). This server would not pass code review.
Hyper-fast context retrieval
Get contextual memory summary with enterprise analytics
Hyper-fast memory deletion
Delete a memory from the agent's memory store
Search memories with unified multi-tier engine and performance optimization
Hyper-fast memory search
Hyper-fast memory storage
Store information in memory with enterprise-grade performance tracking
Duplicate tool definitions: 'remember', 'recall', 'context', 'forget' each defined twice with nearly identical schemas and descriptions. This creates ambiguity for LLMs and violates single-responsibility principle.
Descriptions are marketing copy, not functional documentation. 'Store information in memory with enterprise-grade performance tracking' and 'Hyper-fast memory storage' do not explain WHAT the tool does, WHEN to use it, or WHAT it returns. Descriptions must be 10-1024 chars and answer: what, when, what-returns.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 38 | 2026-07-28+ | v2 |
Parameter descriptions are trivial (1-3 words: 'Unique agent identifier', 'Search query', 'Max results'). LLMs cannot infer meaning from names alone. Each parameter needs a description explaining format, constraints, and purpose. E.g., 'agentId: Unique agent identifier (UUID format, required for all memory operations)'.
No output schemas documented. LLMs cannot plan downstream calls or extract data without knowing what fields to expect. Document return types for all tools (e.g., 'Returns: {memoryId: string, timestamp: ISO8601, content: string, metadata: object}').
No error handling guidance. Tools do not document what errors can occur, whether they are retryable, or what the LLM should do next. E.g., 'If agentId not found, returns 404 with message: "Agent not found. Create one with remember() first."'