trusted-transcription
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| transcribeA | Transcribe an audio file using Whisper. Returns segments with timestamps, text, and confidence scores. |
| detect_hallucinationsA | Run all hallucination detectors on a transcript. Returns a list of flags with severity, detector name, and evidence. Detectors: repetition_loop, silence_hallucination, prompt_echo, temporal_drift, phantom_subtitle, language_switch, completeness. |
| repairA | Run the LLM repair loop on flagged segments. The LLM can delete fabricated segments, replace them, or decline to touch them. Returns structured repair actions with confidence and reasoning. |
| scoreA | Compute quality metrics: WER, CER, hallucination rate, words per minute. If a reference transcription is provided, computes accuracy. |
| estimate_costA | Estimate processing cost for a given audio duration. Returns breakdown: Whisper API cost, LLM repair cost (if needed), total. |
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 5 tools
Each tool maps to a distinct stage in the transcription workflow: transcribe, detect hallucinations, repair, score, and estimate cost. There is no functional overlap or ambiguity about which tool to select.
Most tools follow a verb_noun pattern (detect_hallucinations, estimate_cost), while transcribe, repair, and score are single verbs. The naming is still consistent in style and domain, with no mixed casing or confusing variations.
Five tools is well-scoped for an audio transcription quality pipeline. Each tool serves a clear purpose and the set is neither bloated nor thin.
The toolset covers the full core pipeline: transcription, hallucination detection, LLM-based repair, quality scoring, and cost estimation. A minor gap is the lack of separate transcript retrieval or manual editing tools, but repair covers corrections reasonably.