list_warehousesList this workspace warehouses with id, name, address and phone. A product lives in at most one warehouse, and the warehouse decides the inventory-tracking default and which sales channels new products are listed on. Use a warehouse id here as the warehouseId for create_product.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds domain-level behavioral context: a product lives in at most one warehouse, and the warehouse dictates inventory defaults and sales channels for new products. This goes beyond the safety annotations and helps the agent reason about the data model.
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 two sentences with no filler. The first sentence states the core function, and the second adds necessary domain context and a usage pointer. It is front-loaded and every clause earns its place.
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 simple list tool with no output schema, the description specifies exactly which fields are returned and how the results feed into another tool (create_product). It also explains the significance of warehouses in the system. Nothing essential for an agent to invoke it correctly is missing.
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?
There are zero parameters, and schema coverage is trivially 100%. The description does not need to explain parameters. The baseline for parameterless tools is 4, and the description adequately covers what it lists without referencing parameters.
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 clearly states the tool lists this workspace's warehouses with specific fields (id, name, address, phone). It is unambiguous about the resource and action, and it stands apart from sibling list tools by explicitly naming the warehouse entity.
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 provides a concrete usage hint: 'Use a warehouse id here as the warehouseId for create_product.' It also scopes the tool to 'this workspace,' which is important context. However, it does not explicitly contrast with alternative tools like list_workspaces, so guidance on when not to use it is missing.
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.
Tools are generally separated by entity and action, with helpful cross-references such as get_marketplace vs list_channel_products vs check_channel_readiness. The main soft spots are update_product vs adjust_stock for stock changes and the many list_/search_/get_ triplets, but descriptions mostly make the boundaries clear.
All tools follow a consistent lowercase snake_case verb_noun pattern: create_*, get_*, list_*, search_*, update_*, transition_deal, configure_pipeline, and so on. Despite the large count, the naming is highly predictable.
52 tools is an extreme surface for an agent to reason over, even though the underlying domain is broad. The set could be consolidated substantially without losing core functionality.
Most core lifecycle workflows are covered across leads, contacts, companies, products, orders, and activities. However, there are notable gaps: deals cannot be updated except by stage transition, activities cannot be edited or rescheduled, and there are no delete operations at all, only status-based archiving.