RealRun
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_tasksA | List the everyday tasks RealRun has tested AI models on, with item counts, test dates, results pages and playbook prices. |
| get_scoresA | Headline scores for every model RealRun tested on a task, best first, with cost tier and the metric definition. |
| recommend_modelA | Recommend which AI model to use for a task given a priority: 'accuracy', 'cost' or 'balanced'. Never recommends models RealRun flagged as not recommended. |
| get_warningsB | Models RealRun flagged as not recommended or needing caution on a task (scam failures, invented data, inconsistency). |
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 4 tools
Each tool has a clearly distinct purpose: listing tasks, getting detailed scores, getting recommendations, and getting warnings. There is no overlap in functionality; an agent can easily select the right tool based on the need.
All tool names follow a consistent verb_noun pattern: list_tasks, get_scores, recommend_model, get_warnings. The convention is predictable and uniform.
With only 4 tools, the server is well-scoped for its purpose of providing AI model evaluation and recommendations. Each tool earns its place and the count is neither too thin nor too heavy.
The tool set covers the full lifecycle: discovering tasks (list_tasks), comparing models (get_scores), getting a tailored recommendation (recommend_model), and checking warnings (get_warnings). There are no obvious missing operations for this domain.