neuron_product_membersList the members of a membership product: current (paid-up) or all. Returns each buyer's status (active / lapsed / cancelled), when their period renews, whether they're in the attached WhatsApp group (if one is attached), plus totals.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product UUID (must be a membership product) | |
| status | No | 'active' (current members, default) or 'all' (every member ever) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, idempotent, and non-destructive. The description adds useful behavioral detail by enumerating returned fields: buyer status (active/lapsed/cancelled), renewal period, WhatsApp-group membership when attached, and totals. This is consistent with the read-only annotations.
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?
The description is concise, front-loaded with the action and target resource, and uses its second sentence to list the key return values. Every sentence earns its place without repeating schema or annotation details.
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?
There is no output schema, so the description compensates by summarizing the return content: statuses, renewal timing, WhatsApp-group flag, and totals. This is sufficient for a simple read-only listing tool, though exact response structure and pagination are not specified.
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?
The input schema already fully documents both parameters: id must be a membership product UUID, and status is an enum with 'active' (default) or 'all'. The description's 'current (paid-up)' adds slight nuance but does not materially expand on the schema coverage.
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 starts with a specific verb and resource: 'List the members of a membership product'. It clearly distinguishes this tool from generic member tools such as neuron_list_members by scoping it to product members and by defining the current/all options.
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?
The description gives clear invocation context: use this when you need membership-product members, their status, renewal timing, WhatsApp-group membership, and totals. It does not explicitly name alternative tools or state when not to use it, so it falls short of full routing guidance.
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.