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Framework Silhouette Enterprise Multi-Agent System - a multi-agent orchestration platform with specialized teams for business development, cloud services, code generation, communications, context management, customer service, design/creative, finance, and HR functions
Scoring was not performed
Naming convention inconsistency: Tools mix camelCase (addContext, getContext) with snake_case (create_ticket, list_tickets). MCP spec and production tools standardize on snake_case. This forces LLMs to disambiguate, increasing hallucination risk.
Descriptions in Spanish for customer service, design, and manufacturing tools (e.g., 'Crear nuevo ticket de soporte', 'Listar clientes', 'Crear nueva línea de producción'). LLM context defaults to English; Spanish descriptions are inaccessible and force agent reasoning in wrong language.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 19 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 48 | - | v1 |
No visible input schema validation or JSON Schema format for most tools. The provided tool specs list parameters but lack formal schema definitions (type constraints, required field declarations, pattern validation). For example, 'priority' accepts enum ['low','medium','high','urgent'] but no visible schema enforcement in code.
No output schema documentation. Tools like get_agent_performance, get_manufacturing_dashboard, getSystemOverview have empty input parameters but no documented return structure. LLMs cannot plan downstream calls without knowing response fields.
No error handling or recovery guidance visible in code. If a ticket assignment fails (agent not found, permission denied), the system has no documented recovery path. LLMs need 'Try search_agents() first' type hints.
File references in tool metadata (e.g., 'customer_service_team/main.py', 'design_creative_team/main.py') suggest Python implementation, but package.json indicates Node.js/JavaScript. Language mismatch raises question of whether metadata is stale or misleading.
Parameter descriptions generic or minimal. Examples: 'Team identifier' (12 chars, below 20-char rubric minimum), 'Message importance score (0-1), default 0.8' lacks explanation of how importance affects ranking. LLMs cannot infer parameter semantics from vague labels.
No visible tool chaining support documented. For example, if assign_ticket requires an agent_name, there's no hint that search_agents() or list_team_members() should be called first. Breaking chains force discovery calls and waste tokens.
No idempotency documentation. Tools like create_ticket, create_customer, create_production_order have no visible request deduplication (e.g., idempotency keys). If an agent retries on ambiguous failure, duplicate records will be created.
Tools accept opaque IDs only (customer_id, project_id, ticket_id). No support for human-readable identifiers (customer name, project name, ticket title). Requires extra lookup calls and mismatch with natural chat language ('Show me John's tickets' vs 'Show me tickets for customer_id=C1234').