Multi-tenant MCP server with 138 AI personas, Meta-MCP orchestration for external MCPs, Trinity auto-routing, Swarm engine, RAG system, and hybrid intelligence. Exposes framework via Model Context Protocol with authentication, rate limiting, and RLS.
Critical gaps across naming, descriptions, and schemas. Tool names lack consistent verb prefixes (e.g., 'orchestrate', 'query_rag' are vague). Most parameter descriptions are present but generic. Input schemas visible for all 19 tools but lack depth, many parameters missing type constraints, enums, or validation rules. No output schemas documented. Error handling absent. Security concerns: OAuth tokens, API keys, and tenant data exposed as parameters without clear secret injection patterns. Composition issues: tools like 'orchestrate' and 'call_external_mcp' combine multiple concerns. This server would not pass production code review.
Call an external MCP tool through the Meta-MCP orchestrator
Send a chat message with optional persona and context
Create a new API key for the current tenant
Request a device code for OAuth Device Flow authentication
Poll for device token after user authorization in OAuth Device Flow
Verify device authorization status
Generate 384-dimensional embeddings for semantic memory and context retrieval
Vague tool names lack action clarity. 'orchestrate' does not convey what action occurs; 'query_rag' is domain-specific jargon. LLMs cannot infer intent from these names alone.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract required fields. E.g., 'list_personas' returns 138 personas but structure is undocumented.
Security: API keys, OAuth tokens, and tenant secrets exposed as tool parameters (create_api_key, device_code_request, device_token_poll). No evidence of server-side secret injection. Credentials will leak into agent logs.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 52 | <=2025-11-25 | v2 |
Get system status including MCP server health, loaded personas, and external MCPs
Get information about the current tenant
Get multi-tenant status including usage, quotas, and API key information
Get usage statistics and quota information for the current tenant
List API keys for the current tenant
List all available AI personas (138 specialized personas across domains)
Orchestrate multiple personas for complex multi-step tasks using Trinity routing and Swarm execution
Query the RAG system for context-aware answers using vector memory and semantic search
Revoke an API key
Update settings for the current tenant
Invoke a specialized AI persona to handle a task with domain expertise
Validate user input against configured validation rules
No error handling guidance. Tools lack recovery hints (e.g., 'If persona not found, call list_personas first'). Agents cannot self-correct on failures.
Parameter descriptions are generic and lack constraints. E.g., 'strategy' in orchestrate accepts 'trinity', 'swarm', 'consensus' but no enum declared. 'validation_level' in validate_input lacks format guidance. LLMs will hallucinate invalid values.