neuron_audio_contactTranscribe an audio recording and extract contact information (name, phone, email, notes, tags). Automatically saves the extracted contact to the organization's address book.
| Name | Required | Description | Default |
|---|---|---|---|
| audioBase64 | Yes | Base64-encoded audio data containing spoken contact information | |
| audioFormat | No | Audio format hint for decoding (default: 'webm') | webm |
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.
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.
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.
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.
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.
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.
Add one secure layer between your agents and this server.
Most tools are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.
The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.
309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.
The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.