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Extract Contact from Audio

neuron_audio_contact

Transcribe an audio recording and extract contact information (name, phone, email, notes, tags). Automatically saves the extracted contact to the organization's address book.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audioBase64YesBase64-encoded audio data containing spoken contact information
audioFormatNoAudio format hint for decoding (default: 'webm')webm

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=false (write operation) and destructiveHint=false. The description adds that the tool 'automatically saves' the contact, which is consistent with a non-destructive write. No contradictions, but missing details on duplicate handling or error behavior. Annotations lower the bar, so 4 is appropriate.

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?

Two sentences, 22 words. Extremely concise with no waste. Front-loads the main actions (transcribe, extract, save).

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

Completeness2/5

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

No output schema exists, but the description fails to explain what the tool returns. It mentions saving the contact but not the response format. Also missing audio size limits or error handling. Gaps in completeness for a tool with no output schema.

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?

Schema coverage is 100%, so baseline is 3. The description doesn't add meaning beyond what the schema provides: audioBase64 is base64 audio, audioFormat is format hint. No examples or additional constraints.

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 the tool transcribes audio and extracts contact information (name, phone, email, notes, tags), then automatically saves it. The verb 'transcribe' and 'extract' are specific, and the tool distinguishes itself from siblings like 'create_contact' by focusing on audio input.

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

Usage Guidelines3/5

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

No explicit guidance on when to use this tool versus alternatives (e.g., if you already have text contact info, use 'create_contact'). The description implies use with audio containing contact details but lacks exclusions or context for when not to use it.

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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