{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-waybarrios-vllm-mlx","slug":"mcp-waybarrios-vllm-mlx","name":"vllm-mlx","type":"mcp","url":"https://github.com/waybarrios/vllm-mlx","page_url":"https://unfragile.ai/mcp-waybarrios-vllm-mlx","categories":["mcp-servers"],"tags":["anthropic","apple-silicon","audio-processing","claude-code","computer-vision","image-understanding","inference","llm","machine-learning","macos","mllm","mlx","multimodal-ai","speech-to-text","stt","text-to-speech","tts","video-understanding","vision-language-model","vllm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-waybarrios-vllm-mlx__cap_0","uri":"capability://text.generation.language.openai.compatible.text.inference.with.continuous.batching","name":"openai-compatible text inference with continuous batching","description":"Exposes a FastAPI server implementing OpenAI's /v1/completions and /v1/chat/completions endpoints, backed by a vLLM-style continuous batching scheduler that dynamically groups requests into batches and executes them on Apple Silicon MLX kernels. The scheduler maintains a request queue, allocates KV cache pages on-demand, and interleaves token generation across multiple requests to maximize GPU utilization without blocking on individual request completion.","intents":["Drop-in replace OpenAI API calls with a local Apple Silicon inference server","Run multiple concurrent text generation requests with minimal latency overhead","Serve LLM inference without cloud dependencies or API costs"],"best_for":["Developers building LLM applications on MacBooks with M1/M2/M3/M4 chips","Teams needing local inference for privacy-sensitive workloads","Solo developers prototyping with Llama, Qwen, or similar models"],"limitations":["Throughput capped by Apple Silicon GPU memory bandwidth (~400 tokens/sec typical); slower than cloud APIs for latency-critical applications","No distributed inference across multiple machines; single-machine constraint","Requires model quantization or smaller models to fit in unified memory (16-24GB typical)"],"requires":["Python 3.9+","macOS 12+ with Apple Silicon (M1/M2/M3/M4)","MLX framework installed (pip install mlx)","Model weights in MLX-compatible format (GGUF or HuggingFace)"],"input_types":["text prompts","chat message arrays with role/content structure","system prompts"],"output_types":["text completions","streaming token chunks (Server-Sent Events)","structured JSON with usage statistics"],"categories":["text-generation-language","inference-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_1","uri":"capability://tool.use.integration.anthropic.compatible.messages.api.with.tool.calling","name":"anthropic-compatible messages api with tool calling","description":"Implements Anthropic's /v1/messages endpoint with native support for tool_use blocks, allowing models to request external tool execution via structured JSON schemas. The server parses tool definitions, validates model-generated tool calls against the schema, and integrates with the Model Context Protocol (MCP) to execute tools and return results back to the model in a multi-turn conversation loop.","intents":["Use Claude-compatible tool calling patterns with local models on Apple Silicon","Build agentic workflows where models can call external APIs, databases, or custom functions","Maintain conversation state across multiple tool invocations and model responses"],"best_for":["Developers migrating from Anthropic's hosted Claude to local inference","Teams building AI agents that need deterministic tool execution","Applications requiring tool calling without cloud API dependencies"],"limitations":["Tool calling quality depends on model capability; smaller models may generate malformed tool calls","No built-in tool execution sandboxing; requires external validation of tool arguments","MCP integration requires separate MCP server setup; not all tools are pre-integrated"],"requires":["Python 3.9+","Model with tool-calling capability (Llama 3.1+, Qwen, or similar)","Tool definitions provided as JSON schemas in request","Optional: MCP server running separately for tool execution"],"input_types":["messages array with role/content","tools array with name/description/input_schema","system prompts"],"output_types":["text responses","tool_use blocks with name/id/input","streaming message deltas"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_10","uri":"capability://automation.workflow.server.configuration.and.model.loading.with.auto.quantization","name":"server configuration and model loading with auto-quantization","description":"Provides CLI and programmatic configuration for server startup, model selection, and quantization strategy. Automatically detects available GPU memory, selects appropriate quantization (4-bit, 8-bit, or full precision) based on model size and available memory, and loads models into MLX with optimized memory layout. Supports model discovery from HuggingFace Hub with automatic format conversion.","intents":["Start inference server with minimal configuration","Automatically select quantization strategy based on available hardware","Load models from HuggingFace Hub without manual conversion"],"best_for":["Developers wanting quick server setup without deep MLX knowledge","Teams deploying vllm-mlx across different Apple Silicon hardware","Users experimenting with multiple models without manual quantization"],"limitations":["Auto-quantization may not be optimal for all models; manual tuning may improve quality","Model loading time varies by model size and disk speed (typically 10-60 seconds)","HuggingFace Hub access required for model discovery; offline mode requires pre-downloaded models"],"requires":["Python 3.9+","macOS 12+ with Apple Silicon","Sufficient disk space for model weights (7B model ~4-8GB, 13B ~8-16GB)"],"input_types":["model name (HuggingFace format)","quantization strategy (auto/4bit/8bit/full)","server configuration (port, host, etc.)"],"output_types":["running inference server","model loading logs with memory usage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_11","uri":"capability://automation.workflow.streaming.response.collection.with.server.sent.events","name":"streaming response collection with server-sent events","description":"Implements Server-Sent Events (SSE) streaming for all generation endpoints, allowing clients to receive tokens as they are generated without waiting for completion. The server maintains per-request token buffers, flushes tokens at configurable intervals, and handles client disconnections gracefully. Supports both text and multimodal streaming with consistent message formatting.","intents":["Stream model outputs in real-time for responsive user interfaces","Build chat applications with token-by-token display","Reduce perceived latency by showing partial results during generation"],"best_for":["Web applications requiring real-time model output display","Chat interfaces with token-streaming UI","Applications where user experience depends on streaming feedback"],"limitations":["Streaming adds ~5-10ms latency per token due to serialization and network overhead","Client disconnection handling may leave orphaned generation processes; requires cleanup","Network buffering can delay token delivery; no guaranteed delivery timing"],"requires":["Python 3.9+","HTTP client supporting Server-Sent Events (most modern browsers/libraries)","Network connectivity between client and server"],"input_types":["generation requests with stream=true parameter"],"output_types":["Server-Sent Events stream with JSON-formatted tokens","completion event with final statistics"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_12","uri":"capability://automation.workflow.error.recovery.and.resilience.with.request.retry.logic","name":"error recovery and resilience with request retry logic","description":"Implements automatic error recovery for transient failures (OOM, timeout, model errors) with exponential backoff retry logic. Failed requests are queued for retry with configurable retry counts and backoff strategies. The scheduler tracks request state and can resume interrupted generations from checkpoints, reducing wasted computation.","intents":["Automatically recover from transient GPU memory errors without user intervention","Retry failed requests with exponential backoff to avoid thundering herd","Resume interrupted generations from checkpoints to minimize wasted computation"],"best_for":["Production deployments requiring high availability","Long-running inference jobs prone to transient failures","Teams needing automatic error recovery without manual intervention"],"limitations":["Retry logic increases latency for failed requests (exponential backoff adds 1-30 seconds)","Checkpoint-based recovery requires additional disk I/O; not suitable for real-time applications","Some errors (model bugs, invalid input) are not retryable; requires error classification"],"requires":["Python 3.9+","Disk space for checkpoints (optional, ~100MB per checkpoint)","Configured retry policy (max retries, backoff strategy)"],"input_types":["failed requests with error information","retry configuration (max_retries, backoff_factor)"],"output_types":["retried request results","error logs with retry history"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_13","uri":"capability://automation.workflow.performance.monitoring.and.benchmarking.with.metrics.collection","name":"performance monitoring and benchmarking with metrics collection","description":"Collects detailed performance metrics including tokens-per-second throughput, latency percentiles (p50/p95/p99), GPU memory utilization, and cache hit rates. Exposes metrics via Prometheus-compatible endpoint and provides CLI benchmarking tools for model comparison. Tracks per-request metrics and aggregates them for system-wide analysis.","intents":["Monitor inference server performance in production","Benchmark different models and quantization strategies","Identify performance bottlenecks and optimization opportunities"],"best_for":["Teams deploying vllm-mlx in production requiring observability","Developers comparing model performance across hardware","Operations teams monitoring inference server health"],"limitations":["Metrics collection adds ~1-2% overhead to inference latency","Prometheus endpoint requires separate scraping; no built-in time-series storage","Benchmarking results vary with system load; requires isolated testing environment"],"requires":["Python 3.9+","Prometheus client library (optional, for metrics export)","Benchmarking models and test data"],"input_types":["inference requests (metrics collected automatically)","benchmark configuration (model, batch size, sequence length)"],"output_types":["Prometheus metrics (text format)","benchmark reports (JSON or CSV)","performance dashboards (Grafana-compatible)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_2","uri":"capability://image.visual.multimodal.inference.with.vision.and.video.understanding","name":"multimodal inference with vision and video understanding","description":"Processes images and video frames through vision-language models (LLaVA, Qwen-VL) by encoding visual inputs into MLX tensors, caching vision embeddings to avoid redundant computation, and fusing visual tokens with text tokens in the model's input sequence. Supports batch processing of multiple images per request and video frame extraction with configurable sampling strategies to balance quality and latency.","intents":["Analyze images and describe their content using local vision-language models","Extract text from images (OCR) or answer questions about visual content","Process video frames to understand temporal sequences or extract key moments"],"best_for":["Developers building computer vision applications on MacBooks","Teams needing local image analysis without cloud vision APIs","Applications processing sensitive visual data that cannot leave the device"],"limitations":["Vision encoding adds 200-500ms latency per image; not suitable for real-time video processing","Video processing limited to frame sampling; no temporal modeling across frames","Vision cache requires additional GPU memory; reduces available capacity for text tokens"],"requires":["Python 3.9+","Vision-language model (LLaVA, Qwen-VL, or similar)","PIL/Pillow for image loading","MLX vision encoder (mlx-vlm package)"],"input_types":["image files (PNG, JPEG, WebP)","base64-encoded images","video files (MP4, MOV)","image URLs (downloaded locally)"],"output_types":["text descriptions","structured JSON with detected objects/text","streaming token responses"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_3","uri":"capability://data.processing.analysis.speech.to.text.transcription.with.streaming.audio.input","name":"speech-to-text transcription with streaming audio input","description":"Accepts audio streams or files, processes them through MLX-based speech recognition models (Whisper or similar), and returns transcriptions with optional timestamp alignment. Supports streaming input via chunked audio frames, allowing real-time transcription as audio arrives without waiting for the full file.","intents":["Transcribe voice recordings or live audio streams locally without cloud STT APIs","Build voice-enabled applications with low latency and privacy","Extract text from audio for downstream NLP processing"],"best_for":["Developers building voice interfaces on macOS","Applications requiring offline speech recognition","Teams processing sensitive audio that cannot be sent to cloud services"],"limitations":["Transcription quality varies by audio quality and background noise; no noise suppression built-in","Streaming transcription has higher latency than batch processing (buffering overhead)","Language support depends on model; multilingual models are larger and slower"],"requires":["Python 3.9+","Audio file or stream in WAV/MP3/FLAC format","MLX speech recognition model (Whisper-MLX or similar)","librosa or similar for audio preprocessing"],"input_types":["audio files (WAV, MP3, FLAC, OGG)","streaming audio chunks (bytes)","raw PCM audio"],"output_types":["text transcription","JSON with timestamps and confidence scores","streaming transcript chunks"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_4","uri":"capability://text.generation.language.text.to.speech.synthesis.with.voice.cloning","name":"text-to-speech synthesis with voice cloning","description":"Converts text to natural-sounding speech using MLX-based TTS models, with optional voice cloning by conditioning on reference audio embeddings. Generates audio waveforms in streaming chunks, allowing playback to begin before synthesis completes. Supports multiple voices and speaking styles through model-specific parameters.","intents":["Generate spoken audio from text for accessibility or voice interface applications","Clone voices from reference audio samples for personalized speech synthesis","Stream audio output for real-time playback without waiting for full synthesis"],"best_for":["Developers building voice-enabled applications on macOS","Accessibility applications requiring natural speech synthesis","Applications needing voice cloning without cloud TTS services"],"limitations":["Synthesis quality depends on model; smaller models may sound robotic or unnatural","Voice cloning requires high-quality reference audio; poor quality references degrade output","Real-time synthesis on Apple Silicon slower than cloud APIs for large batches"],"requires":["Python 3.9+","MLX TTS model (Parler TTS, XTTS, or similar)","Reference audio file for voice cloning (optional)","librosa or similar for audio processing"],"input_types":["text strings","reference audio files (WAV, MP3) for voice cloning","voice/speaker parameters"],"output_types":["audio waveforms (WAV format)","streaming audio chunks","base64-encoded audio"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_5","uri":"capability://memory.knowledge.paged.kv.cache.management.with.prefix.sharing","name":"paged kv cache management with prefix sharing","description":"Implements a memory-efficient key-value cache using logical pages (fixed-size blocks) instead of contiguous tensors, allowing cache reuse across requests with shared prefixes (e.g., system prompts, conversation history). The scheduler tracks cache page allocation, deallocates pages when requests complete, and enables multiple requests to reference the same cached pages without duplication.","intents":["Reduce GPU memory usage when serving multiple requests with shared context","Improve throughput by avoiding redundant computation of shared prompt tokens","Support longer context windows without exceeding GPU memory limits"],"best_for":["Applications with many concurrent requests sharing system prompts or conversation prefixes","Long-context inference scenarios where memory efficiency is critical","Teams optimizing for throughput on memory-constrained Apple Silicon"],"limitations":["Page-based caching adds ~50-100ms overhead per request for page allocation/deallocation","Prefix sharing only benefits requests with identical shared context; no benefit for diverse prompts","Cache fragmentation can occur with variable-length requests, reducing effective memory utilization"],"requires":["Python 3.9+","MLX framework with paged cache support","Sufficient GPU memory for page pool (typically 2-4GB for 7B models)"],"input_types":["request batches with shared prefixes","cache page size configuration"],"output_types":["cache page allocations","memory utilization metrics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_6","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.tool.execution","name":"model context protocol (mcp) integration for tool execution","description":"Integrates with the Model Context Protocol to discover, validate, and execute external tools defined in MCP servers. The server maintains a registry of available tools, translates model-generated tool calls into MCP requests, handles tool execution results, and feeds results back to the model for continued reasoning. Supports both synchronous and asynchronous tool execution with timeout handling.","intents":["Enable models to call external APIs, databases, or custom functions via MCP","Build multi-step agentic workflows where models orchestrate tool calls","Integrate with existing MCP servers (Claude Code, etc.) without custom adapters"],"best_for":["Teams building AI agents with external tool dependencies","Developers integrating vllm-mlx with Claude Code or other MCP-compatible tools","Applications requiring deterministic tool execution with error handling"],"limitations":["MCP server must be running separately; no built-in MCP server hosting","Tool execution latency depends on external service; no timeout guarantees","Tool security relies on MCP server validation; no built-in sandboxing in vllm-mlx"],"requires":["Python 3.9+","MCP server running and accessible (HTTP or stdio)","Tool definitions registered in MCP server","Model with tool-calling capability"],"input_types":["tool definitions from MCP server","model-generated tool calls (JSON)","tool execution results"],"output_types":["tool execution results","error messages with retry information","model responses incorporating tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_7","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Constrains model output to match a provided JSON schema by using guided generation (token masking) during decoding. The server validates the schema at request time, applies constraints to the model's token selection at each step, and returns only valid JSON matching the schema. Supports nested objects, arrays, and type constraints (string, number, boolean, enum).","intents":["Extract structured data from unstructured text with guaranteed valid JSON output","Build reliable data pipelines where model output must conform to a fixed schema","Reduce post-processing overhead by ensuring output validity at generation time"],"best_for":["Data extraction pipelines requiring strict output validation","Applications building structured knowledge bases from text","Teams needing deterministic model output for downstream processing"],"limitations":["Schema constraints reduce model expressiveness; may force suboptimal outputs to match schema","Token masking adds ~10-20% latency overhead per token due to schema validation","Complex nested schemas may significantly constrain generation quality"],"requires":["Python 3.9+","JSON schema definition provided at request time","Model with sufficient capability to generate structured output"],"input_types":["text prompts","JSON schema (JSON Schema format)"],"output_types":["valid JSON matching schema","structured data (objects, arrays, primitives)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_8","uri":"capability://planning.reasoning.reasoning.model.output.parsing.with.thinking.extraction","name":"reasoning model output parsing with thinking extraction","description":"Extracts and parses thinking/reasoning tokens from models like Qwen3 and DeepSeek-R1 that emit intermediate reasoning before final answers. The server identifies thinking block delimiters, separates reasoning from output, and optionally streams thinking tokens separately from final response tokens. Supports multiple reasoning formats and models with configurable parsing strategies.","intents":["Access model reasoning process for interpretability and debugging","Stream thinking tokens separately from final output for UI display","Analyze model reasoning quality for model selection and fine-tuning"],"best_for":["Developers building interpretable AI systems requiring reasoning transparency","Teams evaluating reasoning models for complex problem-solving tasks","Applications where understanding model reasoning is as important as the output"],"limitations":["Thinking extraction only works with models that emit explicit thinking tokens","Thinking token overhead increases latency and memory usage (typically 2-5x longer sequences)","Parsing logic is model-specific; different models require different delimiters"],"requires":["Python 3.9+","Reasoning model (Qwen3, DeepSeek-R1, or similar)","Model-specific thinking token format documentation"],"input_types":["prompts for reasoning models","model-specific thinking format configuration"],"output_types":["thinking tokens (raw or formatted)","final response tokens","structured output with thinking/response separation"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-waybarrios-vllm-mlx__cap_9","uri":"capability://data.processing.analysis.openai.compatible.embeddings.endpoint.with.batch.processing","name":"openai-compatible embeddings endpoint with batch processing","description":"Exposes /v1/embeddings endpoint compatible with OpenAI's embedding API, processing text inputs through MLX-based embedding models to generate dense vector representations. Supports batch processing of multiple texts in a single request, caching embeddings for identical inputs, and returning embeddings in OpenAI's format (array of floats with metadata).","intents":["Generate embeddings locally without cloud API dependencies","Build semantic search or RAG systems with local embedding models","Batch-process large text collections for efficient embedding generation"],"best_for":["Teams building RAG systems on Apple Silicon","Applications requiring embeddings for semantic search or clustering","Developers needing local embeddings for privacy-sensitive data"],"limitations":["Embedding quality depends on model; smaller models may have lower semantic quality","Batch processing latency increases with batch size; optimal batch size ~32-64 texts","No built-in vector database; requires external storage (Pinecone, Weaviate, etc.)"],"requires":["Python 3.9+","MLX embedding model (e.g., sentence-transformers converted to MLX)","Text inputs (strings or arrays of strings)"],"input_types":["text strings","arrays of text strings","batch size configuration"],"output_types":["embedding vectors (float arrays)","OpenAI-compatible embedding objects with metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","macOS 12+ with Apple Silicon (M1/M2/M3/M4)","MLX framework installed (pip install mlx)","Model weights in MLX-compatible format (GGUF or HuggingFace)","Model with tool-calling capability (Llama 3.1+, Qwen, or similar)","Tool definitions provided as JSON schemas in request","Optional: MCP server running separately for tool execution","macOS 12+ with Apple Silicon","Sufficient disk space for model weights (7B model ~4-8GB, 13B ~8-16GB)","HTTP client supporting Server-Sent Events (most modern browsers/libraries)"],"failure_modes":["Throughput capped by Apple Silicon GPU memory bandwidth (~400 tokens/sec typical); slower than cloud APIs for latency-critical applications","No distributed inference across multiple machines; single-machine constraint","Requires model quantization or smaller models to fit in unified memory (16-24GB typical)","Tool calling quality depends on model capability; smaller models may generate malformed tool calls","No built-in tool execution sandboxing; requires external validation of tool arguments","MCP integration requires separate MCP server setup; not all tools are pre-integrated","Auto-quantization may not be optimal for all models; manual tuning may improve quality","Model loading time varies by model size and disk speed (typically 10-60 seconds)","HuggingFace Hub access required for model discovery; offline mode requires pre-downloaded models","Streaming adds ~5-10ms latency per token due to serialization and network overhead","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44264991962781464,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2026-05-02T10:35:05Z"},"community":{"stars":1081,"forks":154,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-waybarrios-vllm-mlx","compare_url":"https://unfragile.ai/compare?artifact=mcp-waybarrios-vllm-mlx"}},"signature":"DjaFqKBe8TINLKwTXILgj0lFHO1ZKtmcHS7ea5mFnqnNfDT44yLEbnnR0lCye0xYmiMBPKpwzk+CzisZw4vKDg==","signedAt":"2026-06-23T03:12:07.859Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-waybarrios-vllm-mlx","artifact":"https://unfragile.ai/mcp-waybarrios-vllm-mlx","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-waybarrios-vllm-mlx","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}