Conversational AI-powered backend for market data queries via TwelveData MCP
ChatTwelve exhibits significant definition quality gaps. While 11 of 12 tools have descriptions (92%), most are generic and under 100 chars, failing to explain WHEN to use each tool or what distinguishes similar operations. Only 3 tools (send_message, stream_message, get_session) have visible input schemas with parameter descriptions. The remaining 9 tools lack documented input schemas entirely, a critical failure for LLM tool selection. Naming is verb-forward (good), but descriptions lack actionable context. No tool documents output schemas. Error handling and recovery guidance are absent. The server conflates session management, chat operations, and prompt management without clear composition boundaries.
Activate a system prompt for use in chat
AI service health check endpoint checking connectivity to OpenRouter and returning AI model configuration
Create a new system prompt
Create a new chat session
Delete a system prompt
Retrieve session details and context
Health check endpoint returning application health status, version, and current timestamp
List all available system prompts
9 of 12 tools lack visible input schemas. Tools like create_session, list_prompts, delete_prompt, activate_prompt, and all health checks have no documented parameter types or constraints. LLMs cannot infer valid inputs without schemas.
Descriptions are generic and lack actionable context. 'Create a new chat session' (create_session) does not explain when to call it vs reusing an existing session, or what a session represents. Descriptions average 50 chars; production baseline is 194 chars.
No output schemas documented. Tools return responses (HealthResponse, MCPHealthResponse, AIHealthResponse visible in code) but LLMs have no visibility into response structure, field names, or types. Agents cannot plan downstream calls.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 42 | <=2025-11-25 | v2 |
MCP server health check endpoint checking connectivity to the TwelveData MCP server
Send a message to the AI and receive a response with market data insights
Stream a message response from the AI with real-time updates
Update an existing system prompt
Destructive tool (delete_prompt) lacks confirmation or dry-run support. No error handling guidance. LLMs may delete prompts without recovery path.
No error handling or recovery guidance in any tool description. LLMs have no guidance on retryability, user-fixable errors, or next steps on failure.