Get Product
neuron_get_productGet a single product by id, with its public URL and fee breakdown.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product UUID |
neuron_get_productGet a single product by id, with its public URL and fee breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product UUID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already flag the operation as read-only, idempotent, and non-destructive, so the description only needs to add value beyond safety. It discloses that the response includes the product's public URL and fee breakdown, which is useful given there is no output schema. It does not discuss not-found behavior, but this is not a serious gap given 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?
One compact sentence with the key action and resource front-loaded, followed by the two distinguishing return details. No filler or redundancy.
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 one-parameter, read-only, idempotent getter with full parameter coverage, the description provides the essentials: what is fetched and what the response contains. The lack of an output schema is partially mitigated by the mention of public URL and fee breakdown. It is adequate for an agent to select and call 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 single parameter id is fully documented in the schema with type, format, and description ('Product UUID'), so the description has no obligation to explain it. The description merely repeats 'by id' without adding new semantic detail. Baseline 3 applies due to 100% schema description 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?
States a specific verb ('Get'), a precise resource ('single product by id'), and the response highlights ('public URL and fee breakdown'). Clearly distinguishes from sibling list_products (single vs. list) and from other get_* tools.
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 alternatives or when-not-to-use guidance is given. The phrase 'single product by id' implies this tool is for fetching one product rather than listing multiple, but the description never names list_products or explains when to prefer this tool over other product-related getters.
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.