tariff_classificationClassifies products into correct HTS codes from text or documents, automating tariff lookup and ensuring customs compliance in real time.
Pricing: {"unit": "credits", "per_run": 2}
| Name | Required | Description | Default |
|---|---|---|---|
| product_description | Yes | Description of the product used to identify HS/HTS codes. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 are minimal (only openWorldHint), so the description carries the burden of behavioral disclosure. It mentions 'real time' and 'automating tariff lookup' but does not describe potential side effects (beyond pricing, which is disclosed), accuracy limitations, or whether the tool may return multiple codes or need additional information. The description is adequate but not rich in behavioral detail.
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 a single, well-structured sentence that front-loads the core purpose. It includes the pricing in a separate JSON block, which is not padding. There is no redundant or filler language, and every word contributes to understanding the tool.
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?
Given the tool's simplicity (one parameter, clear purpose), the description covers the essential aspects. It does not explain return values, but an output schema exists, so that is not required. It lacks explicit limitations (e.g., what happens if the product cannot be classified), but overall it is sufficiently complete for a straightforward classification tool.
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 schema covers the parameter fully (100% coverage) as 'Description of the product used to identify HS/HTS codes.' The description adds value by indicating the tool can process 'text or documents,' implying the input can be sourced from a document. It also mentions 'automating tariff lookup,' which hints at the output being HTS codes. These nuances go slightly beyond the schema, warranting a 4.
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's purpose: it classifies products into correct HTS codes. It uses a specific verb ('classifies') with a clear resource ('products') and outcome ('HTS codes'), and further differentiates from siblings like tariff_calc by emphasizing classification rather than calculation. The phrase 'automating tariff lookup and ensuring customs compliance' adds context that distinguishes it from other business data 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?
The description implies usage: when you need to classify a product from a description or document to get HTS codes. It provides clear context (e.g., 'from text or documents') but does not explicitly state when not to use it or name alternatives like tariff_calc. However, the context is sufficient for an agent to infer appropriate use cases, and no exclusions are needed given the tool's specificity.
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 have clearly distinct purposes, with consistent scopes (e.g., chain_* vs park_* vs company_* vs gov_data_*). The list/num pairs are clearly differentiated. A few overlapping concepts exist (e.g., company_patent vs enterprise_change_innovation) but descriptions clarify the angle. Some typos (company_randomin_spection) don't cause ambiguity.
Naming follows a mostly predictable snake_case pattern with prefixes indicating domain (chain_, park_, company_, enterprise_change_, gov_data_, poi_data_, business_surrounding_, cbd_surrounding_). Most tools use <prefix>_<entity>_<action> or <prefix>_<subject>. A few outliers (sg_chokepoint, tariff_calc, corporate_exception_report) deviate but are few and recognizable.
With 198 tools, this is far beyond any reasonable scope for a single server. It exceeds even the 'extreme mismatch' threshold of 50+ tools. The large number makes selection and discoverability challenging, despite good internal organization.
The tool surface covers a vast range of enterprise data, regional macro stats, POI details, supply chain analysis, and tariffs. It appears to cover the primary domain comprehensively, with only minor potential gaps (e.g., no direct tool for company debt ratings or specific product catalogs, but these are addressed via enterprise_change_* and company_* tools).