AI Workbench 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
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 |
|---|---|
| workbench_open_runC | Create a Workbench run folder and initial evidence artifacts. |
| workbench_select_modelC | Select a Workbench model tier and write model_selection.json. |
| workbench_select_policy_packB | Recommend an advisory Workbench policy pack from task metadata. |
| workbench_record_executionC | Capture Goose/model response text into Workbench evidence artifacts. |
| workbench_validate_runC | Run deterministic Workbench validation over a run directory. |
| workbench_quality_gateD | Run the Workbench quality gate for a run directory. |
| workbench_analyze_runsB | Analyze local Workbench run ledgers and write report artifacts. |
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 7 tools
Each tool has a clearly distinct purpose: analyzing runs, opening runs, quality gating, recording execution, selecting model, selecting policy pack, and validating runs. No overlap in functionality.
All tools share the 'workbench_' prefix and mostly follow a verb_noun pattern (e.g., analyze_runs, open_run). Minor deviation with 'quality_gate' which is noun_verb, but overall consistent.
With 7 tools, the set is well-scoped for an AI workbench run management domain. Not too few to lack utility, not too many to be unwieldy.
The tools cover core actions like opening, analyzing, recording, and validating runs, but lack lifecycle management tools such as listing, closing, or deleting runs. Some gaps exist for a complete workflow.