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Glama

Get Live Session

get_live_session

Poll while an EEG/BCI session runs to get live predictions, signal quality, indicators, and running stats. Use it to monitor deployed model sessions and keep the session alive.

Instructions

Live snapshot of a streaming session: latest prediction + recent window, signal quality (meanChannelQuality, snrDb, artifactProbability), indicators, running stats. Poll this while a session runs. Live telemetry requires a DEPLOYED model session (hub deploy / playback with a classifier); modelless hardware streams have no telemetry — use stream_status for those. Expect low confidence during filter/ASR warm-up (first seconds); quality < 0.5 or high artifactProbability means the signal is poor. 404 => session not active in this backend. Each poll also feeds the idle watchdog (see start_stream's idle_timeout_sec), keeping an actively watched session alive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo
session_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully carries the burden and does so well: it discloses a side effect (each poll feeds the idle watchdog, keeping the session alive), error semantics (404 => session not active in this backend), warm-up behavior (low confidence during filter/ASR warm-up), and interpretation thresholds (quality < 0.5 or high artifactProbability = poor signal). Only auth requirements are unstated, which is minor.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but well-structured: payload contents, usage, prerequisites, thresholds, errors, and side effects in order. Slightly packet-heavy, but essentially every sentence carries actionable information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be restated; the description focuses on what an agent can't get elsewhere — prerequisites, error codes, signal-quality interpretation, and the watchdog side effect. Complete for a polling telemetry tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and neither parameter is documented. The 'window' parameter (default 50) is never explained — the phrase 'recent window' is not tied to it, nor does the description say how its value changes behavior. At 0% coverage the description must compensate and it largely does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (live snapshot of a streaming session) and enumerates the payload contents. It explicitly distinguishes itself from the sibling stream_status by noting modelless hardware streams have no telemetry, so an agent can route without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ('Poll this while a session runs'), an explicit when-not ('modelless hardware streams have no telemetry — use stream_status for those'), and precondition (requires a DEPLOYED model session). Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.