Validate an EORI number (Economic Operators Registration and Identification) against the official EU customs database (live lookup). An EORI is required for EU imports/exports — check a trading partner's or your own EORI before customs filings / freight bookings. Input e.g. 'DE1234567890123' (country code + number).
| Name | Required | Description | Default |
|---|---|---|---|
| eori | Yes |
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?
No annotations are provided, so the description bears full responsibility. It mentions 'live lookup' against the official EU customs database, indicating an external call, but does not disclose idempotency, read-only nature, side effects, rate limits, authentication requirements, or return value format.
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 three sentences long, each sentence adding distinct value: definition, usage context, and input example. It is front-loaded with the main action and contains no redundant information.
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?
While the description explains what the tool does and when to use it, it lacks an explanation of the output format (e.g., valid/invalid, error messages). Since no output schema exists, this omission leaves the agent uncertain about what to expect after the call.
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 has 0% description coverage; the description adds critical meaning by providing an example ('DE1234567890123') and explaining the format ('country code + number'). This goes beyond the schema's 'type: string' and is helpful for correct usage.
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 action 'Validate an EORI number' against the official EU customs database, specifies the type of identifier (EORI), and provides context about its use for EU imports/exports. It effectively distinguishes from siblings by focusing on a specific EU customs identifier.
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 clear usage context: 'check a trading partner's or your own EORI before customs filings / freight bookings.' It implies the tool should be used for pre-filing verification but does not explicitly compare to sibling validation tools like validate_eu_vat, nor does it state when not to use it.
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.
Each tool targets a distinct type of validation or calculation (email domain, payout, reorder point, payment split, ABA routing, EORI, EU VAT, GTIN, IBAN, VAT rate). There is no overlap in purpose, and descriptions clarify the specific identifier or operation.
Most tool names use a verb_noun pattern (e.g., validate_aba_routing, check_email_domain), but some are noun phrases (payout_reconciliation, reorder_point, stripe_connect_split, vat_rate_by_country). While clearly descriptive, the verb-prefix inconsistency slightly reduces predictability.
With 10 tools, the server covers a focused set of commerce validation and calculation tasks without being too sparse or overwhelming. Each tool serves a clear, independent purpose.
The tool set covers key commerce validations (email, bank routing, IBAN, VAT, EORI, GTIN) plus useful calculations (payout, reorder, payment split, VAT rates). Minor gaps exist (e.g., phone/address/credit card validation, currency conversion), but the coverage is coherent for the stated domain.