Optuna MCP Server
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| create_studyC | Create a new Optuna study with the given study_name and directions. |
| get_all_study_namesC | Get all study names from the storage. |
| askC | Suggest new parameters using Optuna |
| tellC | Report the result of a trial |
| set_samplerA | Set the sampler for the study. The sampler must be one of the following: - TPESampler - NSGAIISampler - RandomSampler - GPSampler |
| set_trial_user_attrC | Set user attributes for a trial |
| get_trial_user_attrsC | Get user attributes in a trial |
| set_metric_namesB | Set metric_names. metric_names are labels used to distinguish what each objective value is. |
| get_metric_namesD | Get metric_names |
| get_directionsC | Get the directions of the study. |
| get_trialsA | Get all trials in a CSV format |
| best_trialA | Get the best trial |
| best_trialsA | Return trials located at the Pareto front in the study. |
| add_trialC | Add a trial to the study. |
| add_trialsC | Add multiple trials to the study. |
| plot_optimization_historyA | Return the optimization history plot as an image. |
| plot_hypervolume_historyC | Return the hypervolume history plot as an image. |
| plot_pareto_frontA | Return the Pareto front plot as an image for multi-objective optimization. |
| plot_contourB | Return the contour plot as an image. |
| plot_parallel_coordinateB | Return the parallel coordinate plot as an image. |
| plot_sliceC | Return the slice plot as an image. |
| plot_param_importancesA | Return the parameter importances plot as an image. |
| plot_edfC | Return the EDF plot as an image. |
| plot_timelineB | Return the timeline plot as an image. |
| plot_rankA | Return the rank plot as an image. |
| launch_optuna_dashboardC | Launch the Optuna dashboard |
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 26 tools
Each tool has a clearly distinct purpose. The many plot tools are differentiated by the type of plot they generate, and the core tools like ask, tell, create_study, etc. have unique roles without overlap.
Most tools follow a verb_noun pattern (add_trial, create_study, get_trials), but a few deviate like 'ask', 'tell' (single verbs), and 'best_trial' (adjective_noun). Overall consistent enough for readability.
26 tools is above the typical range of 3-15, but each tool serves a clear purpose within the Optuna ecosystem. The count is justified by the breadth of functionality including many visualization tools.
The tool set covers the full lifecycle of hyperparameter optimization: creating studies, adding trials, suggesting/reporting results, retrieving best trials, setting samplers, and extensive analysis with plots. No obvious gaps.