neuron_duplicate_productClone an existing product into a hidden draft copy (new checkout link, '(copy)' title, reset counters). A 'pool' code product is cloned with an empty pool — restock codes before enabling. Edit the copy with neuron_update_product, then set enabled: true to publish.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product UUID to clone |
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?
The description adds substantial behavior beyond the annotations: the clone is hidden, not published, receives a new checkout link and reset counters, and pool-code products start with an empty pool. It also implies the original product remains intact by describing the copy as a separate draft. No contradiction with the 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?
Two dense, useful sentences with no filler. The main behavior is front-loaded, and the second sentence packs the pool-code exception and follow-up workflow efficiently.
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?
For a single-parameter action, the description is complete: it explains the side effects, the special pool-code behavior, and the follow-up update/publish steps. No output schema is present, but an agent has enough information to select and invoke the tool correctly.
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 only parameter, id, is already fully documented in the schema as 'Product UUID to clone', so schema description coverage is 100%. The description adds contextual meaning ('existing product') but no new syntax, format, or additional parameter guidance, matching the baseline for high 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 names a specific action (clone), a resource (existing product), and precise outcomes: hidden draft copy, new checkout link, '(copy)' title, and reset counters. It clearly distinguishes this from create/update/delete product operations and frames the duplication lifecycle.
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?
It gives clear usage context: clone an existing product into a draft, then edit with neuron_update_product, then publish by setting enabled: true. The pool-code edge case adds an important conditional instruction. It does not explicitly contrast with create_product or state when not to use duplication, but the workflow guidance is strong.
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.