This server exposes 3 tools via HTTP but has critical definition gaps. chat_completions has a reasonable description (156 chars) and input schema with types, but parameters lack individual descriptions, LLMs cannot infer what 'model', 'messages', or 'stream' control. list_models and health have descriptions but empty input schemas ({}), violating the requirement that every parameter be described. No output schemas are documented for any tool. The server is not an MCP server, it is a FastAPI HTTP wrapper around a LangGraph agent. It does not implement the MCP protocol (no tool registration via MCP, no _meta, no capabilities negotiation). Scoring it as an MCP server is technically incorrect, but evaluated against the rubric: tool definitions are incomplete and lack LLM-friendly guidance.

• Add descriptions to every parameter in chat_completions: 'model' (which model to use; defaults to tcga-analyst), 'messages' (array of {role, content} objects; role must be 'user', 'assistant', or 'system'), 'stream' (if true, returns server-sent events; if false, returns complete response).
• Document the output schema for chat_completions: return type is {id, object, created, model, choices: [{index, message: {role, content}, finish_reason}], usage: {prompt_tokens, completion_tokens, total_tokens}}.
• For list_models and health, explicitly define input schema as {} with a note that no parameters are accepted, or remove them if they are not meant to be exposed as tools.
• Add error handling guidance: if the ClickHouse query fails, return a structured error with the SQL that was attempted and the validation error, so the LLM can understand what went wrong.
• If this is intended as an MCP server, implement the MCP protocol: register tools via the MCP server interface, include _meta in responses, and negotiate capabilities. If it is only an OpenAI-compatible HTTP endpoint, do not label it as MCP.