Prompt Auto-Optimizer MCP
Server 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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| gepa_start_evolutionC | Initialize evolution process with configuration and seed prompt |
| gepa_record_trajectoryC | Record execution trajectory for prompt evaluation |
| gepa_evaluate_promptC | Evaluate prompt candidate performance across multiple tasks |
| gepa_reflectC | Analyze failures and generate prompt improvements |
| gepa_get_pareto_frontierC | Retrieve optimal candidates from Pareto frontier |
| gepa_select_optimalC | Select best prompt candidate for given context |
| gepa_create_backupC | Create system backup including evolution state and trajectories |
| gepa_restore_backupC | Restore system from a specific backup |
| gepa_list_backupsB | List available system backups |
| gepa_recovery_statusC | Get comprehensive disaster recovery status and health information |
| gepa_recover_componentC | Recover a specific GEPA component |
| gepa_integrity_checkC | Perform comprehensive data integrity check |
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
Most tools have distinct purposes, but some overlap exists: gepa_evaluate_prompt and gepa_record_trajectory both relate to prompt evaluation, and gepa_select_optimal and gepa_get_pareto_frontier both involve selecting optimal candidates. The descriptions help clarify differences, but an agent might occasionally confuse these pairs.
All tool names follow a consistent 'gepa_verb_noun' pattern with snake_case, using clear verbs like create, evaluate, get, list, record, recover, restore, select, and start. This predictability makes it easy for agents to understand and use the toolset.
With 12 tools, the server is well-scoped for prompt auto-optimization, covering key areas like evaluation, evolution, backup, recovery, and selection. Each tool has a clear role, and the count is neither too sparse nor overwhelming for the domain.
The toolset provides comprehensive coverage for prompt optimization workflows, including evolution (start_evolution), evaluation (evaluate_prompt, record_trajectory), selection (select_optimal, get_pareto_frontier), reflection (reflect), and disaster recovery (backup, restore, integrity_check, recovery). A minor gap is the lack of tools for modifying or deleting components, but core operations are well-covered.