OpenXAI MCP Server
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 |
|---|---|
| list_datasetsB | List available datasets in OpenXAI framework |
| load_datasetC | Load a specific dataset from OpenXAI |
| list_modelsB | List available pre-trained models in OpenXAI |
| load_modelC | Load a pre-trained model from OpenXAI |
| list_explainersC | List available explanation methods in OpenXAI |
| generate_explanationC | Generate explanations for model predictions using OpenXAI explainers |
| list_metricsB | List available evaluation metrics in OpenXAI |
| evaluate_explanationC | Evaluate explanation quality using OpenXAI metrics |
| get_leaderboardC | Get leaderboard results for explanation methods |
| get_framework_infoC | Get information about OpenXAI framework |
| get_deployment_guideC | Get step-by-step guidance for deploying models using OpenXAI Studio |
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 11 tools
Each tool has a clearly distinct purpose with no ambiguity. The tools cover different aspects of the OpenXAI ecosystem: explanation generation/evaluation, framework information, data/model management, and leaderboard access. There is no overlap in functionality between tools like 'evaluate_explanation' and 'generate_explanation' or between listing functions and loading functions.
All tools follow a consistent verb_noun naming pattern using snake_case throughout. The naming convention is predictable with verbs like 'evaluate', 'generate', 'get', 'list', and 'load' consistently paired with appropriate nouns. This makes the tool set easy to understand and navigate.
With 11 tools, this server is well-scoped for its purpose of providing access to OpenXAI's explainable AI framework. The count is appropriate as it covers key operations like explanation generation/evaluation, framework information retrieval, dataset/model management, and leaderboard access without being overwhelming or insufficient.
The tool set provides comprehensive coverage for interacting with the OpenXAI framework, including explanation workflows, data/model access, and framework information. The only minor gap is the lack of tools for modifying or updating datasets/models (e.g., 'update_model' or 'create_dataset'), but the existing tools cover the core read-oriented operations well for this domain.