score-studio-mcp
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| SCORESTUDIO_URL | No | Optional override for the Score Studio API endpoint. Defaults to https://api.scorestudio.ai. | https://api.scorestudio.ai |
| SCORESTUDIO_TOKEN | Yes | Score Studio bearer token or API key required for authentication. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| scorestudio_whoamiA | Return the authenticated Score Studio user and organization memberships. |
| scorestudio_list_datasetsC | List versioned datasets in a Score Studio organization. |
| scorestudio_get_datasetA | Get a dataset and its version lineage by organization and slug. |
| scorestudio_list_modelsA | List models and versions in a Score Studio organization. |
| scorestudio_start_trainingA | Start a tracked training job against an immutable dataset version. This may consume compute credits. |
| scorestudio_list_evaluationsA | List measured evaluation runs and their current states. |
| scorestudio_start_evaluationA | Create an immutable measured evaluation run for a model version and dataset version. |
| scorestudio_get_evaluation_reportA | Get metrics, slices, intervals, and evidence for an evaluation run. |
| scorestudio_list_deploymentsB | List deployments, revisions, and runtime state in an organization. |
| scorestudio_verify_deploymentA | Run and retain deployment health verification evidence without changing the active revision. |
| scorestudio_list_workflowsB | List typed Score Studio workflow definitions and status. |
| scorestudio_run_workflowC | Run a workflow against an existing Score Studio image object. The result may report deferred blocks. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 12 tools
Each tool targets a distinct resource or action: auth, workflows, datasets, models, training, evaluations, and deployments. The similar-looking start/run tools are clearly separated by their object types.
Tools consistently use the scorestudio_ prefix with snake_case verb_noun names. Minor deviations like whoami and run_workflow vs start_training are easy to predict but not perfectly uniform.
With 12 tools, the server is well-scoped for an MLOps platform covering auth, workflows, datasets, models, training, evaluations, and deployments. Each tool has a clear purpose and none feel redundant.
Core read and start operations are present, but there are gaps: models only support listing, training jobs cannot be fetched after starting, and deployment management is limited to listing and verification. Agents may hit dead ends when trying to check training results or detailed model state.