Validate IBANs in 111 countries; bank directory checks for DE, AT, BE, FR, LU, NL, PT and ES.
Scored across 4 tools
The two list tools and two get tools are functionally identical, differing only by prefix. An agent cannot reliably determine whether to call quiqqer_mcp_skill_get or quiqqer_skill_get, though list vs get is at least clear.
The set mixes quiqqer_mcp_skill_* and quiqqer_skill_* naming conventions for the same operations, and the server name 'IBAN Validation MCP' has no relation to QUIQQER or skill retrieval. No predictable naming pattern emerges.
Four tools is not an unreasonable number, but the surface is padded with two duplicate pairs that reduce the effective count to two. The count is acceptable only if the redundancies are treated as accidental.
Given the server name, users would expect IBAN validation operations, but the tools only retrieve QUIQQER skill metadata and instructions. There are no validation, format-checking, or account-related operations at all.
Returns the skill metadata and full instruction body by name. Read the instructions before performing the matching task.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does reveal that the tool returns both metadata and full instruction body, which is helpful. However, it does not disclose error behavior, name uniqueness, or whether repeated calls are expected, leaving some ambiguity for a read operation.
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 primary function is front-loaded, and the usage reminder is concise and actionable. Every word 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 single-parameter retrieval tool, the description is nearly complete. It states what is returned and suggests a usage context. It does not mention enumerating available names via a list tool, but this is a minor gap given the simplicity and the existence of sibling list tools.
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%, so the description must compensate. It adds the meaning that the 'name' parameter is the name of the skill to retrieve. This is minimal but sufficient for a single string parameter; the tool's behavior is otherwise straightforward.
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 states a specific verb and resource: 'Returns the skill metadata and full instruction body by name.' This clearly identifies the tool as a getter. However, it does not distinguish itself from the sibling quiqqer_skill_get, which likely serves a very similar purpose.
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 instruction to 'Read the instructions before performing the matching task' implies this should be used before executing a related task. It provides useful context but does not explicitly state when to choose this tool over quiqqer_skill_get or list tools, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists AI skills provided by installed QUIQQER packages. Use at the start of non-trivial QUIQQER tasks when package-specific instructions may apply. Match the returned descriptions to the task and load the relevant skills with quiqqer_mcp_skill_get before making changes.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of disclosing behavior. It implies a read-only discovery operation through 'Lists' and 'before making changes,' but it does not explicitly state that the tool has no side effects or describe what each returned entry contains beyond being a description.
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 and well structured: it states the purpose, gives the usage context, and explains the next step. Every sentence earns its place, and the most important information is front-loaded.
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 listing tool with one optional parameter it conveys the workflow well, but it leaves the category parameter undocumented and does not clarify how this tool differs from quiqqer_skill_list. The lack of an output schema is only partially compensated by the mention of 'returned descriptions.'
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%, and the description never mentions the 'category' parameter. An agent is left to guess whether category is a filter, a grouping key, or something else, and no acceptable values or format are given.
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 ('Lists') and the resource ('AI skills provided by installed QUIQQER packages'). It is distinct enough from the 'get' siblings, but it does not explicitly differentiate itself from the similarly named quiqqer_skill_list.
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 gives explicit context for when to use the tool: 'at the start of non-trivial QUIQQER tasks when package-specific instructions may apply.' It also names the follow-up tool, quiqqer_mcp_skill_get, but does not state when not to use it or compare it with quiqqer_skill_list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the skill metadata and full instruction body by name. Read the instructions before performing the matching task.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool is a retrieval operation and adds context that the result contains instructions to be read before the task. With no annotations, however, it does not explicitly state the absence of side effects, error behavior, or any access requirements.
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, front-loaded with the core return behavior and a short usage directive. Both sentences earn their place, though the phrase 'matching task' is somewhat vague.
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 with no output schema or annotations the description must carry more context. It fails to distinguish itself from the near-identical sibling 'quiqqer_mcp_skill_get' and does not clarify the shape or size of the returned instruction body.
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 'name' parameter (0% coverage), and the description only restates 'by name' rather than explaining what names are valid, matching semantics, or format. This leaves the agent with almost no additional parameter guidance.
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 identifies a concrete action ('Returns') and resource ('skill metadata and full instruction body') and specifies the lookup key ('by name'). It is clear in isolation, but it does not differentiate from the near-identical sibling 'quiqqer_mcp_skill_get'.
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 directive 'Read the instructions before performing the matching task' implies this tool should be used to fetch instructions before executing a task. There is no explicit when-not-to-use guidance or mention of alternatives such as the skill-list tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists AI skills provided by installed QUIQQER packages. Use at the start of non-trivial QUIQQER tasks when package-specific instructions may apply. Match the returned descriptions to the task and load the relevant skills with quiqqer_skill_get before making changes.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No |
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 does clearly indicate a read-style list operation and notes that results are descriptions, and 'before making changes' implies non-mutating intent. However, it does not disclose return structure, filtering behavior, pagination, or any permission/side-effect caveats.
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 short sentences with the purpose front-loaded. Every clause earns its place: purpose, usage context, and recommended next action. There is no filler or 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?
For a simple one-optional-parameter list tool, the description provides solid what/when/next guidance and mentions 'returned descriptions,' which helps despite the lack of an output schema. However, the missing `category` semantics and lack of differentiation from `quiqqer_mcp_skill_list` leave noticeable gaps.
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%, and the tool description never mentions the `category` parameter. An agent cannot tell what values are valid, whether `category` filters or groups results, or how the default `null` behaves. The parameter name and type only weakly imply an optional filter.
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 first sentence uses a specific verb, 'Lists', and a specific resource, 'AI skills provided by installed QUIQQER packages', so an agent can tell what the tool does. It loses the top score because the sibling `quiqqer_mcp_skill_list` appears to cover the same action and the description does not differentiate between them.
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 states when to use the tool: 'at the start of non-trivial QUIQQER tasks when package-specific instructions may apply.' It also names the follow-up action and alternative tool, `quiqqer_skill_get`, and instructs the agent to load relevant skills before making changes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
quiqqer_mcp_skill_getquiqqer_mcp_skill_listquiqqer_skill_getquiqqer_skill_listCheck the bank behind an IBAN before you pay: bank-code check, BIC with source, bank-level sanctions
IBAN validation, extraction, format specs and BIC/SWIFT lookup tools for AI assistants.
Validate IBAN bank account numbers (format + mod-97 checksum). Returns whether the IBAN is valid.
OpenIBAN MCP — validate an IBAN's checksum + bank code and resolve the
Add one secure layer between your agents and this server.
