This is not an MCP server. The repository is a full-stack ERP/manufacturing application (Carbon) written in TypeScript and Rust, with React frontends, databases, Docker deployment, and traditional web infrastructure. The codebase shows workspace configuration, dependency management, build pipelines, and containerization, not MCP tool definitions. The single 'tool' entry (createMessage) appears to be a misclassification or metadata artifact; there is no evidence of an actual MCP server implementation, tool registration, or protocol compliance anywhere in the provided source. The description 'MCP sampling capability for requesting LLM completions from the client' references deprecated MCP Sampling (removed in 2026-07-28), and the tool definition lacks any visible input schema, output documentation, or error handling. The file path 'apps/erp/package.json' does not contain tool definitions, it lists npm dependencies for a React app. No MCP-specific code (e.g., mcp package imports, server startup, tool registration handlers, message routing) is visible.

• Confirm whether this is actually an MCP server repository. If not, this evaluation is invalid. If it is, provide the actual MCP server implementation code.
• If MCP server exists, locate and provide the tool registration code (typically server.tool() or server.call_tool handler).
• Replace deprecated Sampling with direct LLM API calls. Sampling was removed from the MCP spec in 2026-07-28. Instead, call OpenAI/Anthropic/etc. APIs directly from the server or integrate via a tool that wraps those APIs.
• Add complete input and output schemas using proper JSON Schema format with type declarations for all fields (e.g., 'messages': { 'type': 'array', 'items': { 'type': 'object', 'properties': {...} } }).
• Expand tool description from current 1-2 lines to 50-150 characters explaining: What does it do? When should the LLM call it instead of another tool? What structure does it return?
• Document output schema explicitly: 'Returns an object with fields: completion (string), model (string), tokens_used (number), cost (number). Include example output structure.
• Add error handling with recovery guidance. E.g., 'If model_id is invalid, call list_available_models() first. If API timeout occurs, retry with a shorter max_tokens limit.'
• Add parameter constraints: modelId should be an enum of supported models, or at minimum document valid format. system and messages should specify type=string/array/object with nested structure.
• Declare tool scope and permissions (e.g., 'requires: llm:read, rate_limit: 10 calls/min'). This enables least-privilege agent configuration.