Server Quality Checklist

Tool Scores

• lookup_by_krsA
  4.2/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the burden of behavioral disclosure. It makes clear that this is a read operation ('Returns'), that the data is the current registry entry, and enumerates the returned fields. It does not cover error or not-found behavior, but for a read-only lookup tool this is reasonably transparent.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is compact and front-loaded: the first sentence states the action and identifier, and the second sentence provides a useful field list. There is no filler, redundancy, or irrelevant detail.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple two-parameter lookup tool with no output schema, the description gives sufficient return detail to set agent expectations. It lacks explicit edge-case behavior such as invalid or nonexistent KRS numbers, but the core invocation semantics are well covered.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so both krs and register already have detailed descriptions including format, enum values, and default. The MCP description adds no parameter-specific semantics beyond what the schema already provides, so the baseline score of 3 applies.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description uses a specific verb ('Look up') and a specific resource ('Polish organization by KRS number in the Krajowy Rejestr Sądowy'), which clearly distinguishes it from the NIP-based sibling tool. It also states that the tool returns the full current registry entry, making the purpose concrete and action-oriented.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description clearly communicates when to use the tool: when an organization needs to be looked up via KRS number. It does not explicitly mention the alternative lookup_by_nip or provide exclusion conditions, but the KRS-specific scope provides enough context for an agent to select it correctly.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• lookup_by_nipA
  4.5/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description carries the behavioral burden. It conveys a read-only lookup by listing returned data and source, and adds the important historical-status behavior. It stops short of mentioning error/not-found behavior or rate limits, which would make it fully transparent.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Three compact, front-loaded sentences. The first sentence names the action and source, the second lists expected return data, and the third gives practical parameter guidance with no filler.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple two-parameter lookup with no output schema, the description covers what it does, what it returns, and when to use the optional parameter. An agent has enough to invoke it correctly.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining why the date parameter matters (historical status and tax deductibility) and clarifying the default behavior.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  States a specific action ('Look up a Polish company by NIP'), names the authoritative resource (Biała Lista), and enumerates the returned fields. The by-NIP scope clearly differentiates it from the sibling lookup_by_krs, so an agent can select it without opening schemas.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Gives clear context for the optional date parameter and why it matters ('tax deductibility of costs paid to that vendor'). It does not explicitly state 'use lookup_by_krs instead when you have a KRS', but the identifier-based distinction is strongly implied by the name and description.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.