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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
ASTRA_SSE_PORTNoSSE transport port9002
ASTRA_HTTP_PORTNoStreamable HTTP port9003
ASTRA_LOG_LEVELNoLog level: debug, info, warn, errorinfo
ASTRA_CORS_ORIGINNoCORS allowed origin*

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

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": true
}
resources
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_system_statusC

ASTRA System Status

get_metricsC

Real-time Metrics

get_snn_stateC

SNN Engine State

snn_stepC

Advance SNN Simulation

snn_resetB

Reset SNN Engine

inject_spikesD

Spike Injection

get_acm_scoreC

Consciousness Assessment (Proxy)

check_ethicsC

IRB Neural Welfare Check

set_parameterD

Modify State Parameter

get_platform_statusD

Bio-Computing Platforms

export_snapshotC

Full State Snapshot

simulation_controlD

Simulation Control

wm_encodeB

Encode SNN State to Latent Space

wm_predictC

Predict Next SNN State in Latent Space

wm_planC

CEM Planning for Optimal Spike Injection

wm_surpriseC

Violation-of-Expectation Detection

wm_train_stepD

Online World Model Training Step

wm_statusC

World Model Status & Metrics

sensor_visualC

V-JEPA 2 Visual Encoding (Image/Video)

sensor_audioB

A-JEPA Audio Encoding (Waveform → Mel → Latent)

sensor_olfactoryC

Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)

sensor_fuseD

Cross-Modal Attention Fusion

sensor_processC

Full Multimodal Pipeline (All Modalities → Fused z)

sensor_statusC

Multimodal Sensor Pipeline Status

Prompts

Interactive templates invoked by user choice

NameDescription
wm-experimentWorld Model experiment: encode → predict → compare → plan
multimodal-experimentFull multimodal sensor experiment: visual + audio + olfactory → fused → WM
system-health-reportComprehensive system health report
snn-experimentControlled SNN experiment
ethics-stress-testProgressive biomarker degradation

Resources

Contextual data attached and managed by the client

NameDescription
wm-latentCurrent latent space state and embedding history
wm-predictionsWorld Model prediction history and accuracy
sensors-stateMultimodal sensor pipeline state and last fusion
metrics-realtimeLive metrics
snn-topologySNN network architecture
acm-stateConsciousness proxy assessment
ethics-welfareIRB compliance report
snapshot-currentComplete state dump

TDQS

C2.3/5.0

Scored across 24 tools

Disambiguation3/5

Many status/metrics tools overlap conceptually: get_system_status, get_metrics, get_snn_state, get_platform_status, wm_status, and sensor_status all report some form of state or health. Additionally, sensor_fuse and sensor_process have borderline responsibilities, though wm_* and sensor_* prefixes help distinguish the two main subsystems.

Naming Consistency3/5

The naming is organized by subsystem prefixes like get_*, wm_*, and sensor_*, but the conventions are mixed: snn_step and snn_reset are command-like, simulation_control is noun-like, and inject_spikes is verb_noun. There is no single consistent pattern across the full tool set, though the prefix grouping keeps it readable.

Tool Count3/5

24 tools sits in the heavy range and requires agents to navigate several distinct subsystems: core SNN simulation, world model, sensors, status, and ethics. The count is defensible given the breadth of the platform, but it is borderline and each tool needs to justify its place.

Completeness4/5

The tool surface covers a broad lifecycle: SNN stepping/reset, spike injection, parameter changes, snapshots, world model training/prediction/planning, and multimodal sensor processing. Minor gaps exist, such as no explicit SNN parameter retrieval or sensor data ingestion control, but agents can mostly achieve the platform's apparent goals without dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues