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Extract Contact from Audio

neuron_audio_contact

Transcribe base64-encoded audio to extract and auto-save contact details: name, phone, email, notes, and tags.

Instructions

Transcribe an audio recording and extract contact information (name, phone, email, notes, tags). Auto-saves the contact. Provide base64-encoded audio data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audioBase64YesBase64-encoded audio data
audioFormatNoAudio format hint (default: webm)webm
Behavior2/5

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

No annotations are provided, so the description must disclose all behavioral traits. It states 'Auto-saves the contact,' which implies mutation, but it does not specify required permissions, rate limits, or what happens to the audio after processing. Lack of output schema leaves the agent unsure of the return value. The description is insufficiently transparent.

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 consists of two clear sentences with no fluff. It front-loads the main action and includes the input requirement. One point off for not structuring the auto-save behavior more explicitly, but overall concise.

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

Completeness3/5

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

Given the tool has only two parameters and no output schema, the description covers the purpose and input adequately. However, it does not specify error conditions, supported audio formats, or what the tool returns (if anything). It is minimally complete but could be improved.

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 description coverage is 100%, so the schema already documents both parameters. The description adds no new semantic details beyond what the schema provides (e.g., base64-encoded audio, format hint). Baseline score of 3 is appropriate.

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's purpose: 'Transcribe an audio recording and extract contact information (name, phone, email, notes, tags).' The verb 'transcribe' and 'extract' are specific, and the resource is well-defined. It distinguishes itself from sibling tools like neuron_create_contact by handling audio input and auto-saving.

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

Usage Guidelines4/5

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

The description explains when to use the tool: when you have audio with contact info to extract. It does not explicitly mention when not to use it or provide alternatives, but given no other tool does transcription+extraction, the context is clear. Minor improvement would be noting manual contact creation as an alternative.

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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