ultratribe-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 | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| system_diagnosticsA | Returns hardware info, CUDA capability, VRAM, and UltraTribe framework status. |
| benchmark_inferenceC | Runs synthetic in-memory benchmark to test model speed (ms/batch), throughput, and VRAM footprint. |
| list_supported_studiesA | Lists available neural decoding studies (Algonauts 2025, BOLD5000, Wen 2017, Lebel 2023). |
| get_atlas_regionsC | Retrieves Human Connectome Project (HCP) cortical or Harvard-Oxford subcortical ROI regions. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| System Status | Live health and hardware resource utilization |
| Studies Catalog | Supported fMRI neuroscience benchmarks |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: system diagnostics, inference benchmarking, listing supported datasets, and fetching atlas regions. There is no meaningful overlap between any two tools.
Three tools follow a clear verb_noun pattern (benchmark_inference, list_supported_studies, get_atlas_regions), but system_diagnostics breaks the pattern by using a noun phrase. The naming remains readable and mostly consistent.
Four tools is a reasonable size for a specialized server, but the set feels slightly minimal for the breadth of 'neural decoding' hinted at by the study and atlas tools. Still, each tool has a clear role and the count is not problematic.
The tools cover diagnostics, benchmarking, study listing, and atlas region lookup, but there are no tools to fetch study details, access actual neural data, or run decoding workflows. This creates notable gaps if the server is meant to support end-to-end neural decoding research.