F
licenseA
qualityB
maintenanceEnables AI agents to connect to the NeuralForgeAI cluster for inspecting and controlling YOLO training jobs, including launching, monitoring, canceling studies, and verifying dataset paths.
8
-
ultralytics-mcp
by amanharshx
Related Servers
Alternatives to ultralytics-mcp
No user-submitted related servers found.
Related Servers
Rockfish MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables interaction with the Rockfish AI platform for synthetic data generation, dataset management, and ML workflow orchestration through tools like TabGAN training and Manta analytics.MIT
Coreshub MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables interaction with the CoreHub cloud platform to manage containers, EPFS filesystems, distributed training, and inference services through MCP tools.10MIT- FlicenseNot gradedqualityDmaintenanceEnables programmatic management of a Dify instance, including datasets, apps, and tools.-
- AlicenseAqualityDmaintenanceEnables AI systems like Claude and Cursor to directly manage Railway projects, deployments, services, environment variables, and monitor logs through natural language commands.98 npmMIT
- FlicenseNot gradedqualityDmaintenanceEnables interaction with the Unstructured API to manage data workflows, including creating and managing source connectors (S3, Azure, Google Drive, etc.), destination connectors (Weaviate, Pinecone, MongoDB, etc.), and workflows for document processing and data pipelines.-
TDQS
B3.4/5.0
Scored across 44 tools
Disambiguation4/5
Most tools target clearly distinct resources and actions, and descriptions explicitly separate similar surfaces like training_monitor, model_metrics, and model_plots. Minor ambiguity remains between dataset_export and model export tools (export_create/status/list/cancel), and between auto_annotate_stop, export_cancel, and training_cancel, but boundaries are documented.
Naming Consistency3/5
The set is mostly snake_case with a resource_verb pattern (projects_list, datasets_get, deployment_logs), but singular/plural prefixes are inconsistent across related resources (project/projects, dataset/datasets, model/models, deployment/deployments, export/exports). A few names break the pattern entirely (gpu_availability, explore_projects/datasets), making naming readable but not fully predictable.
Tool Count2/5
With 44 tools, the surface is heavy for an MCP server and well above the typical 3-15 range. The domain is broad, but many related operations could be consolidated or grouped, so the count feels excessive rather than tightly scoped.
Completeness3/5
Coverage is strong for datasets, models, exports, training, and auto-annotation, including CRUD-like operations and lifecycle controls. However, notable gaps remain: deployments can be stopped but not started, created, or resized, and project/dataset update or rename operations are absent.
Maintenance
ActivityMaintained
ResponsivenessWithin a week