This server exposes only one tool, 'chat', which is a handler for HTTP POST requests rather than a discrete, composable agent tool. The tool definition lacks proper MCP structure: no input schema with typed parameters is visible in the provided source code, descriptions are generic and fail to explain WHAT the tool does or WHEN to use it, and there is no documented output schema. The tool appears to be an HTTP endpoint wrapper rather than a properly defined MCP tool following the 54 agentic patterns. The description mentions 'streaming support' and 'Vercel AI protocol' but does not explain the tool's actual purpose, prerequisites, or return value. Parameter documentation is minimal: 'model' and 'builtinTools' are listed but lack constraints, ranges, enums, or validation guidance. The server is a JupyterLab extension, not a dedicated MCP agent tool server, which explains the mismatch between the MCP tool definition rubric and the actual codebase structure.

• Define a proper JSON Schema for the 'chat' tool input. Include type definitions (string, array, etc.), required fields, constraints (min/max length, enum values), and validation rules. Example: {"type": "object", "properties": {"model": {"type": "string", "enum": ["gpt-4", "claude-3"], "description": "The LLM model to use..."}, "builtinTools": {"type": "array", "items": {"type": "string"}, "description": "Array of tool names enabled..."}}}}
• Rewrite the tool description to be LLM-optimized (80-200 characters). State the purpose clearly: 'Send a chat message and receive streaming AI responses with tool calls, reasoning steps, and citations. Use this to ask questions or request actions from the AI agent.'
• Add a comprehensive description to every parameter. For 'model', explain valid values, how selection affects cost/latency/quality. For 'builtinTools', explain which tools are available and why you might enable/disable them.
• Rename 'chat' to a more specific verb_noun pair. Candidates: 'send_chat_message', 'stream_ai_response', 'invoke_agent'. Match the actual action performed.
• Document the output schema explicitly. List all returned fields (e.g., 'response', 'tool_calls', 'reasoning_steps', 'citations'), their types, and what each contains. Indicate if the response is streamed or a single object.
• Add error handling guidance in the tool description or separate error documentation. Examples: 'If model is invalid, returns error code 400 with list of available models. Try again with a valid model name.' 'If a tool in builtinTools is not installed, returns error 422. Check available tools with list_tools().'