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OpenAI and Anthropic compatible LLM inference server for Apple Silicon, powered by MLX
This MCP server is NOT a traditional MCP server exposing domain tools. Instead, it is a utility library providing 12 internal helper functions for converting between MCP tool schemas and various LLM-specific formats (OpenAI, DeepSeek, Qwen, Llama, etc.). The tools serve as transformation/adapter functions rather than domain-facing operations. Most lack comprehensive parameter descriptions, error handling guidance, and return schema documentation. The naming is reasonably clear (verb-noun patterns like 'convert_tools_to_typescript', 'parse_tool_calls'), but descriptions are terse (20-100 chars, below the 194-char baseline for production tools). Parameter schemas are present but minimally documented. No tool annotations (readonly/destructive hints), no error guidance, and no pagination for list-returning tools. This is a low-maturity adapter library, not a production-grade MCP server.
Convert OpenAI JSON Schema tool definitions to TypeScript namespace format for Harmony/GPT-OSS models.
Extract tool calls from model response.
Extract tool calls by trying all known formats: Gemma 4, DeepSeek-V4 DSML, Mistral, Qwen bracket, Qwen/Hermes XML, Llama, Nemotron, and raw JSON.
Extract tool calls from DeepSeek V3 and R1 model output with special unicode tokens.
Format tool result for inclusion in conversation messages.
Format multiple tool results as messages.
Check if response contains tool calls.
Parameter descriptions are minimal or missing context. Most parameters have only 1-3 word descriptions ('MCPTool instance', 'OpenAI tool call from model response'). LLMs cannot infer how to construct or use these objects from such terse docs. No examples, no structure guidance.
No return/output schemas documented. Tools return objects (MCPToolResult, formatted messages, lists of tool calls) but downstream callers have no visibility into the structure. This forces callers to infer or reverse-engineer the response format.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 59 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Convert MCP tool schema to OpenAI function calling format.
Convert list of MCP tools to OpenAI format.
Merge MCP tools with user-provided tools. User tools take precedence if there are name conflicts.
Parse OpenAI tool call back to MCP format.
Parse tool calls from model output. Supports multiple formats: MiniMax, Qwen3 bracket, Qwen XML, Llama, Nemotron, and raw JSON.
No tool annotations (readonly/destructive/idempotent hints). All 12 tools are READ_ONLY, but this is only stated in the metadata, not in the tool definitions themselves. Modern MCP specs expect structured annotations in the tool schema.
No error handling or recovery guidance. Tools do not document what errors are possible, how to interpret them, or what to do next. E.g., parse_tool_calls could fail if the text contains no tool calls, but there is no documented error response.
Parameter type definitions are incomplete. Input schemas declare parameter types as 'object' or 'array', but nested properties lack type and description detail. E.g., merge_tools accepts 'mcp_tools' as array but does not describe the structure of each array element.
Duplicate tool names without clear distinction. 'extract_tool_calls' appears 3 times (once generic, once in AutoToolParser, once in DeepSeekToolParser). LLMs cannot disambiguate between them based on names alone. Should use qualified names like 'extract_tool_calls_auto_parser', 'extract_tool_calls_deepseek'.