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paskal

bitrix24-mcp-server

by paskal

bitrix24_call_transcribe_max

Transcribe CRM calls with maximum quality using three local models. Returns raw transcript components for manual reconciliation into a clean transcript.

Instructions

MAX-quality transcription of a CRM call — the highest-fidelity pipeline, for when the basic bitrix24_call_transcribe isn't good enough. Runs THREE local models on the recording and returns the raw materials for you (the calling model) to reconcile into one clean transcript: • GigaAM v2 — Russian-native, never hallucinates, gets domain terms right (the reliable backbone) • Whisper large-v3 with condition_on_previous_text=False + domain hotwords — punctuation + proper nouns • pyannote diarization — speaker turns (who spoke when) Returns {whisper_text, gigaam_text, segments:[{start,speaker,text}], speakers, reconcile_hint}. YOU reconcile: keep Whisper for punctuation/proper-nouns, trust GigaAM where Whisper diverges into non-Russian garbage (hallucination), assign Менеджер/Клиент per speaker from content, fix diarization flips — then save with bitrix24_crm_timeline_note_save. Brand names are auto-normalised (V-LUX / вилюкс → Velux, etc.). REQUIRES: a heavy Python env (faster-whisper + gigaam + pyannote.audio + torch) at B24_MAX_PYTHON, and an HF token (env HF_TOKEN or B24_HF_TOKEN) whose account has accepted the pyannote gated-model terms. If any of that is missing the tool returns a clear, actionable error (error_type: missing_hf_token | missing_deps | model_not_approved) — fix that, then retry. Slower than basic (three models, no pooling).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
activityIdYesThe call activity ID (a VOXIMPLANT_CALL activity)
Behavior5/5

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

Given no annotations, the description fully discloses the tool's internal behavior: it runs three models (GigaAM, Whisper, pyannote), returns raw materials for reconciliation, is slower than basic, and lists potential error types. It also mentions auto-normalization of brand names. This is complete transparency.

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?

The description is relatively long but well-structured with bullet points and clear sections. Each sentence adds value, though it could be slightly more concise without losing important details. The front-loading is effective with the core purpose stated first.

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?

The description covers all necessary context: input (activityId), complex output format (whisper_text, gigaam_text, segments, etc.), post-processing instructions (reconciliation steps), system requirements, error handling, and even notes brand name normalization. It is fully self-contained for an AI agent to use correctly.

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

Parameters3/5

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

With schema coverage at 100%, the description adds minimal new information about the required 'activityId' parameter beyond what the schema provides ('The call activity ID (a VOXIMPLANT_CALL activity)'). The description contextualizes it as part of the broader use case but does not enhance semantic understanding of the parameter itself.

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?

The description clearly states it provides 'MAX-quality transcription of a CRM call' and explicitly distinguishes it from the sibling tool 'bitrix24_call_transcribe' by stating it's for when the basic version isn't good enough. The verb 'transcribe' and resource 'CRM call' are specific.

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?

The description explicitly states when to use this tool ('when the basic bitrix24_call_transcribe isn't good enough'), outlines prerequisites (heavy Python env, HF token), explains error conditions and how to respond, and suggests a follow-up tool (bitrix24_crm_timeline_note_save). This provides comprehensive usage guidance.

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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