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ContextPulse

ContextPulse

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by ContextPulse
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Python
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get_voice_stats

Retrieve dictation quality metrics including words per minute, average Whisper confidence, and correction rate for a specified lookback window. Use to analyze voice performance trends over time.

Instructions

Return dictation quality stats (WPM, confidence average, correction rate) over a window.

Returns aggregate metrics: words-per-minute, average Whisper confidence, correction rate (per get_correction_history), session count.

USE WHEN: the user asks "how is my dictation going" or you're analyzing voice quality trends. NOT FOR: per-segment data — use get_recent_voice or search_voice.

BEHAVIOR: pure read. Returns zero-valued metrics if no voice activity in the window.

PARAMETERS: hours: lookback window. Range 1-720 (30d). Default 24.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv0.1.2
    • addedInput schema / properties / hours
      Added value: +{
+  "default": 24,
+  "title": "Hours",
+  "type": "integer"
+}
  2. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

Without annotations, description fully covers behavioral traits: pure read operation, returns zero-valued metrics if no activity, and no side effects.

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

Conciseness5/5

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

Structured with clear sections, bullet points, and front-loaded summary; every sentence adds value.

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?

Given output schema exists, description covers return metrics, behavior, and usage completely for a stats tool.

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

Parameters5/5

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

Adds significant meaning beyond schema: specifies lookback window, range (1-720), and default (24) for the 'hours' parameter.

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?

Clearly states it returns dictation quality stats (WPM, confidence average, correction rate) over a window, and distinguishes itself from siblings like get_recent_voice and search_voice.

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?

Provides explicit 'USE WHEN' and 'NOT FOR' conditions, naming alternative tools for per-segment data.

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

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