neuron

Bot API Chat

neuron_bot_api_chat

Send a message to a bot via the API and receive a streaming or complete AI-generated response. Requires a valid API key with 'nrn_' prefix.

Input Schema

TableJSON Schema

Name	Required	Description
`apiKey`	Yes	Bot API key with 'nrn_' prefix for authentication
`message`	Yes	The message text to send to the bot
`contactName`	No	Display name of the contact for conversation context
`contactPhone`	Yes	Phone number of the contact sending the message (E.164 format, e.g., '+2348012345678')

TDQS

A3.8/5.0

Behavior4/5

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

Annotations indicate this is not read-only nor destructive, and description adds that it produces a streaming or complete AI-generated response. It also notes the API key requirement. This complements the sparse annotations well, though it does not detail side effects like conversation state changes.

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 two sentences with no unnecessary words. It front-loads the action and key requirement (API key). However, it could be slightly improved by structuring the streaming/response modes more explicitly.

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 no output schema, the description should explain the response format or behavior (e.g., streaming mechanics). It mentions streaming or complete response but lacks details on how the response is delivered. For a chat tool, this is a notable gap.

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 parameters are fully documented in the input schema. The description does not add additional meaning beyond what's in the schema, such as explaining the role of 'contactName' or 'contactPhone' in conversation context. Baseline 3 applies.

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 sends a message to a bot via API and receives an AI-generated response. It distinguishes from siblings like 'neuron_bot_api_send' by specifying the AI response nature, and from 'neuron_bot_api_get_messages' by being a send operation.

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?

The description provides basic usage context (requires API key, sends message), but does not explicitly state when to use this tool over siblings like 'neuron_bot_api_send' or 'neuron_send_message'. No exclusions or alternative recommendations are given.

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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TDQS

B3.4/5.0

Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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