neuron

Bot API List Conversations

neuron_bot_api_list_conversations

Read-onlyIdempotent

Retrieve a paginated list of conversations managed by the bot. Supports filtering by conversation status. Requires a valid API key.

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	Page number for pagination (default: 1)
`limit`	No	Number of conversations per page (default: 20)
`apiKey`	Yes	Bot API key with 'nrn_' prefix for authentication
`status`	No	Filter by conversation status: 'active', 'closed', or 'pending'

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds authentication requirement, pagination, and filtering capability, providing context beyond annotations.

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?

Single sentence with two short clauses; front-loaded with key action and no redundancy. Every word adds value.

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

Completeness4/5

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

Covers core purpose, auth, pagination, and filtering. Lacks details on response structure or pagination defaults, but for a simple list tool it is sufficient.

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 already well-described. The tool description only reiterates 'filtering by conversation status' without adding new meaning for each parameter.

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 retrieves a paginated list of conversations managed by the bot, with filtering by status. It distinguishes from siblings like 'get_conversation' (single) and 'close_conversation' (mutate) by specifying scope and pagination.

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?

Description mentions filtering and API-key requirement, implying when to use. However, it does not explicitly state when not to use or compare with alternatives like 'list_conversations' (non-bot API) or 'get_conversation'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All neuron tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?