Commerce validators: live EU VAT (VIES), EORI, email/MX lookups; IBAN/ABA/GTIN checksums; VAT rates.
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Average 4.1/5 across 10 of 10 tools scored.
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.
check_email_domainAInspectCheck whether a domain can actually receive email (has MX records) via a real DNS-over-HTTPS lookup — validate a customer/supplier email's domain before sending or invoicing. An LLM can't know current DNS; this does the live lookup.
| Name | Required | Description | Default |
|---|---|---|---|
| email_or_domain | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully carries the behavioral burden. It discloses that a real DNS lookup is performed, implying an external call. However, it does not mention rate limits, error handling, or whether it only checks MX records. Adequate but could be more detailed.
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, front-loaded with the core purpose, and adds relevant context without fluff. Every sentence adds value, making it highly concise.
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?
The tool is simple, but the description omits what the tool returns (e.g., boolean or details). Since no output schema exists, this gap reduces completeness. Otherwise, it covers the domain and use case well.
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 'email_or_domain' is explained in the description: it accepts an email or domain and checks its MX records. With 0% schema coverage, the description adds significant meaning. However, it could clarify format expectations (e.g., 'user@example.com' vs 'example.com').
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 verb 'check', the resource 'domain email capability', and the method 'DNS-over-HTTPS lookup'. It explicitly distinguishes from sibling tools by focusing on email domain validation, which is unrelated to financial validations like IBAN or ABA.
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 context: 'validate a customer/supplier email's domain before sending or invoicing' and highlights that 'An LLM can't know current DNS; this does the live lookup.' While it doesn't explicitly mention when not to use, the sibling tools are all different, making usage unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
payout_reconciliationAInspectExplain why a payout is less than sales: walk gross -> deductions -> expected, and (if actual_deposit given) flag the unexplained gap (shortfall/surplus).
| Name | Required | Description | Default |
|---|---|---|---|
| refunds | No | ||
| chargebacks | No | ||
| gross_sales | Yes | ||
| actual_deposit | No | ||
| processing_fees | No | ||
| other_deductions | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes the analysis process but does not explicitly state read-only nature or any side effects. Assumptions about input data (e.g., positive gross sales) are not clarified.
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 sentence, concise, front-loaded with main action. Uses arrows to show flow. No waste.
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 6 parameters, no output schema, and no annotations, description is fairly complete but lacks detail on output format (how 'flag' is presented) and edge cases. Could be more helpful with explicit output description.
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?
Schema description coverage is 0%. Description provides context for parameters by mentioning 'gross', 'deductions', 'expected', and 'actual_deposit', but individual parameters like 'processing_fees' and 'other_deductions' are not explained in detail. Baseline 3 because description adds some value but not complete parameter documentation.
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?
Description clearly states the tool's purpose: to explain why a payout is less than sales by walking through gross, deductions, expected, and flagging gap if actual deposit given. It uses specific verbs and resource, distinguishing it clearly from sibling validation 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?
Implicitly tells when to use (when needing payout discrepancy analysis). No explicit when-not-to or alternatives, but siblings are mostly validation tools, so context is clear. Slight deduct for lack of explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reorder_pointBInspectReorder point = lead-time demand + safety stock. If on_hand is given, returns whether to reorder now and the days of cover remaining.
| Name | Required | Description | Default |
|---|---|---|---|
| on_hand | No | ||
| safety_stock | No | ||
| lead_time_days | Yes | ||
| avg_daily_sales | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description indicates a pure computation with conditional behavior. No annotations provided, so description carries burden. It does not disclose error handling, edge cases, or return type details, but the core behavior is clear.
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 concise sentences, no redundancy, and front-loaded with the formula. Every word adds value.
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?
Tool is simple and compute-only, but lacks output schema. Description vaguely mentions return values without structure (e.g., boolean and number). For a 4-parameter tool with no annotations, more detail would improve completeness.
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?
Schema description coverage is 0%. The description mentions 'lead-time demand' and 'safety stock' but does not explicitly map 'avg_daily_sales' and 'lead_time_days' to lead-time demand, nor explain the 'safety_stock' parameter. Only 'on_hand' is partially explained.
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 computes a reorder point and conditionally returns reorder decision and days of cover based on on_hand. The verb 'returns' and the formula make the purpose unambiguous.
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 guidance on when to use this tool versus alternatives. While siblings are distinct (validations), the description does not provide usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stripe_connect_splitAInspectCompute the Stripe Connect three-way split for one charge.
Returns what the buyer pays, what Stripe takes, what the platform nets (its application fee), and what the connected seller nets — plus the platform's effective take rate. fee_bearer: 'seller' | 'platform' | 'buyer' (who absorbs the Stripe processing fee). Rates are editable; defaults are US card standard 2.9%+$0.30.
| Name | Required | Description | Default |
|---|---|---|---|
| fee_bearer | No | seller | |
| charge_amount | Yes | ||
| processing_pct | No | ||
| processing_fixed | No | ||
| application_fee_pct | No | ||
| application_fee_fixed | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the output (buyer pays, Stripe takes, platform nets, seller nets, take rate) and explains the fee_bearer parameter. It does not mention side effects, but as a calculator this is likely harmless and sufficient.
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 sentences plus a note on fee_bearer. It is relatively concise, but the information could be structured more clearly (e.g., listing return values or parameters). Still, it is not verbose and frontloads the purpose.
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 no output schema, the description describes the return conceptually. It covers the major behavior of the tool. It lacks detailed error handling or edge cases, but for a simple calculator this is acceptable.
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?
Schema coverage is 0%, so the description must compensate. It explains fee_bearer and mentions defaults for processing fees. However, it does not explicitly describe charge_amount, application_fee_pct, or application_fee_fixed. The context provided is helpful but not exhaustive.
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 explicitly states it computes the three-way split for a single Stripe Connect charge. It is a specific verb+resource combination that distinguishes it from sibling tools, which are largely validation or reconciliation 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 clearly implies usage for calculating a Connect split, but does not explicitly state when to use this tool over alternatives or when not to use it. It could be improved by adding a note on prerequisites or cases where this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_aba_routingAInspectValidate a US ABA bank routing number (9 digits) by its checksum. Catch typos before initiating an ACH/wire payout. Pure-algorithm; nothing leaves the machine.
| Name | Required | Description | Default |
|---|---|---|---|
| routing_number | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that validation is algorithmic ('chechsum'), locally executed ('nothing leaves the machine'), and requires a 9-digit string. It could mention the return type or error handling, but for a simple validation, this is sufficient.
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: the first states purpose and method, the second adds usage context and safety guarantee. No redundant words, front-loaded with key 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?
The tool is simple (one parameter, deterministic). The description covers what, why, and safety. It doesn't explicitly state the return value, but that is often implicit for validation tools. Sibling tools provide context for differentiation. Slightly incomplete but adequate.
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 input schema has 0% description coverage, but the description compensates by specifying the parameter is a US ABA routing number with a 9-digit format and checksum validation. This adds essential meaning beyond the schema's title.
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 verb 'Validate' and the resource 'US ABA bank routing number (9 digits)' with the method 'by its checksum'. It distinguishes from sibling tools like validate_iban or validate_eu_vat by specifying the US-specific routing number validation.
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 states the intended use case: 'Catch typos before initiating an ACH/wire payout'. While it doesn't explicitly mention when not to use it or alternatives, the sibling context and specificity imply its usage domain clearly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_eoriAInspectValidate 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 |
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.
validate_eu_vatAInspectValidate an EU VAT number against the official EU VIES service (live government lookup). Returns whether it is registered/valid and, if available, the registered trader name + address. An LLM cannot know this without the real lookup — use this before invoicing/reverse-charging an EU B2B customer. Input e.g. 'DE811569869' or 'IE6388047V' (country code + number).
| Name | Required | Description | Default |
|---|---|---|---|
| vat_number | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool performs a live government lookup, returns validity and optional trader details, and implies no side effects. It does not mention rate limits or failure modes, but the core behavior is transparent.
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 concise with no wasted words. It front-loads the main purpose and includes examples and usage context in a few sentences.
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 moderate complexity and absence of output schema, the description covers the essential aspects: purpose, real-time nature, expected output, and when to use. It does not address potential service unavailability or detailed output structure, but it is sufficient for a validation 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 provides no description for the single parameter 'vat_number' (0% coverage). The description adds value by giving format examples ('DE811569869' or 'IE6388047V' with country code + number), but it does not fully specify allowed country codes or exact length constraints.
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 specifies the action (validate), resource (EU VAT number), and method (official EU VIES live lookup). It includes input format examples and distinguishes from sibling tools like validate_eori and validate_gtin.
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 explicitly tells when to use it (before invoicing/reverse-charging an EU B2B customer) and explains that an LLM cannot know the data without the lookup. However, it does not mention when not to use it or provide alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_gtinAInspectValidate a GTIN / UPC / EAN barcode (GTIN-8/12/13/14) by its check digit. Catches mistyped product barcodes in inventory/catalog workflows. Pure-algorithm.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description notes 'Pure-algorithm', indicating no side effects, external calls, or mutations. Given no annotations, this fully discloses behavior as a safe, read-only computation.
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 complete sentences, no redundancy. Every word adds value: verb, resource, context, and behavioral hint.
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?
Covers purpose, input, and behavior, but lacks output format details (e.g., boolean or validation result). Given no output schema, this is a minor gap.
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 'code' is described as a GTIN such as GTIN-8/12/13/14, adding meaning beyond the schema's minimal 'Code'. This clarifies input format, though exact length/digit constraints are not specified.
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 validates GTIN/UPC/EAN barcodes by check digit, specifying the exact types (GTIN-8/12/13/14). This distinguishes it from sibling tools that validate other entities like email domains or routing numbers.
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 use for checking barcode validity in inventory/catalog workflows, but does not explicitly state when not to use or provide alternatives beyond the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_ibanAInspectValidate an IBAN (International Bank Account Number) by structure + the ISO 7064 mod-97 checksum. Catches typos/invalid accounts before you initiate a transfer. Pure-algorithm; no data leaves the machine.
| Name | Required | Description | Default |
|---|---|---|---|
| iban | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that validation is pure-algorithm and no data leaves the machine, indicating no side effects or external calls. However, it does not mention any limitations or error handling behaviors.
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 concise at three sentences, each adding value: validation method, practical use case, and privacy guarantee. No extraneous 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?
The description covers what, how, and why, and adds privacy context. However, it does not specify the return format or what happens on validation failure (e.g., boolean result vs. error message), which would be helpful given no output schema.
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 and only one parameter (iban). The description does not add any formatting details, examples, or constraints beyond the parameter name. The agent gains no additional understanding of valid input formats.
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 validates IBAN numbers by structure and checksum, and mentions it catches typos before transfers. It is distinct from sibling validation tools like validate_aba_routing or validate_eu_vat due to the specific IBAN focus.
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 use before initiating a transfer but does not explicitly state when to use this tool versus alternatives (e.g., validating other account numbers). No exclusions or comparative guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vat_rate_by_countryAInspectEU VAT rates (standard / reduced / super-reduced / parking) for a country, from the maintained ibericode/vat-rates dataset (fetched live, cached 24h) — including which rate set was in force on an optional 'date' (YYYY-MM-DD) and the names of regional exceptions (e.g. Canary Islands). Input e.g. 'DE', 'FR', 'HU'.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| country_code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses data source, live fetching with 24h cache, optional date for historical rates, and regional exceptions. No annotations exist, so description carries full burden adequately.
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?
Concise, front-loaded with purpose, every sentence adds value without repetition.
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?
Covers input format, data source, caching, and optional parameters; lacks return structure description, but output schema is absent.
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?
Adds essential meaning beyond the schema by specifying country_code format via examples and date format (YYYY-MM-DD) and purpose, compensating for 0% 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?
Clearly states it retrieves EU VAT rates (standard, reduced, super-reduced, parking) for a country, distinguishing it from sibling validation 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?
Provides context of when to use (e.g., getting VAT rates) and gives input examples, though no explicit exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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