RAFSOFT.NET Faktura VAT 2026

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 comparison includes prices (PLN net, one-time purchase) and a feature matrix, which gives insight into the content. However, it does not describe the output format (e.g., table, JSON) or any edge cases. The description is honest and non-contradictory but lacks deeper 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 main action. It efficiently includes key details (specific editions, price type, and feature list) without unnecessary fluff. Every word contributes to understanding the tool's 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 the tool's simplicity (2 optional params, 100% schema coverage, no output schema), the description is quite complete. It covers the purpose, the editions compared, and the type of output (comparison matrix). It could be improved by giving an example of the response structure, but that is not essential for an initial selection.

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 already provides 100% coverage for both parameters (editions array and onlyDifferences boolean), so the baseline is 3. The description adds context about the specific editions and features but does not explain parameter usage beyond what the schema describes. It does not compensate for any gaps, as there are none.

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 function: comparing Faktura VAT 2026 editions. It specifies the exact editions (START, STANDARD, PRO) and what aspects are compared (prices and feature availability). This differentiates it from sibling tools like get_product_info or get_ksef_info, which focus on single items rather than edition comparisons.

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 implicitly guides the user to use this tool when they need a side-by-side comparison of editions. It does not explicitly state when to avoid using it or mention alternative tools, but the context is clear given the tool's name and description. No exclusions are provided, which is acceptable for a straightforward comparison tool.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals the data is in Polish and the internal structure of sources/sections, but does not state the return format, selection behavior, or whether it returns all matching entries. For a read-only tool, it lacks clarity on what the agent will actually receive.

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 long sentence, but it is densely informative: the source names and section lists are necessary for parameter selection. The structure uses clear parentheses and quotes, and there is no filler. It could be slightly more front-loaded with a verb, but it remains efficient.

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 tool with only 2 parameters and no output schema, the description covers the input space thoroughly by providing allowed sections and sources. It does not describe the response format beyond 'question/answer pairs', which is minimal for an agent to know what to expect. Given the simplicity, it's adequate but not fully complete.

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 already documents both parameters with 100% coverage, giving a high baseline. The description adds significant value by enumerating the valid section names for each 'source' enum value, which is not present in the schema itself. This enriches the parameter meaning and helps the agent construct correct queries beyond the bare enum.

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 identifies the resource as the FAQ for Faktura VAT 2026, with Polish question/answer pairs. It lists specific sources and sections, which makes the scope unambiguous and distinguishes it from sibling tools like get_product_info or get_ksef_info. However, it lacks an explicit action verb ('returns', 'retrieves'), relying on the tool name for the operation.

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: it shows the topics covered, so an agent can infer when to call this tool for FAQ-related queries. It does not explicitly state alternatives, exclusions, or compare with search_knowledge_base or other siblings. The granular section list subtly guides selection between 'program' and 'ksef' but gives no direct 'use this when' advice.

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

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

With no annotations, the description carries the transparency burden. It implies a safe informational read (listing content topics), and the topic enum suggests a lookup behavior. However, it does not explicitly state there are no side effects, cost implications, or external network dependencies, and it fails to describe the output 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 two sentences, front-loaded with the tool's purpose and a dense list of covered topics. The second sentence about edition coverage adds useful context but could be seen as slightly extraneous. Overall, it is concise and easy to scan.

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 info tool with a well-defined enum, the description covers the main content areas. However, it does not clarify the distinction from sibling tools like get_faq, and the absence of an output schema means the response format is left unstated. This creates a completeness gap for an agent deciding which tool to invoke.

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 100% with detailed descriptions for each enum value, so the schema already explains the parameter. The description adds context about binding to KSeF but no extra parameter syntax or behavior beyond the enum. This meets the baseline of 3.

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 identifies the tool as providing KSeF 2.0 related information, listing specific topics (integration, invoice types, validator, client features, FAQ). It is specific to KSeF and distinct from generic product info, but uses the generic verb 'info' rather than a more active verb like 'retrieve' or 'explain'.

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 explicit guidance on when to use this tool versus alternatives like get_faq or get_product_info. The KSeF focus is implied but not stated as a criterion, and there is no mention of which sibling might be preferred for non-KSeF queries.

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

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

No annotations are provided, so the description carries the burden. It states the tool returns information and lists the types of data included, but it does not disclose behavior such as default section handling, authentication requirements, or error behavior. The description adds some context beyond the purpose but is minimal for a tool with no annotation coverage.

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, front-loaded sentence that starts with the verb and resource, then packs the key details into a parenthetical. There is no redundant wording or unnecessary filler.

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?

With no output schema, the description lists the return content explicitly (plans, prices, features, download URLs, purchase links), which is sufficient for a simple read-only tool. It is complete enough for the agent to understand what information will be retrieved, though it could mention the default section behavior.

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 100%, with the one parameter (section) having an enum and a clear description including the default. The tool description does not add any parameter-specific semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

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 uses a specific verb ('Returns') and resource ('product and pricing info') and explicitly names the products (Faktura VAT 2026, Mobilna Faktura) and content (plans, prices in PLN, features, download URLs, purchase links). This clearly distinguishes it from siblings like compare_editions, get_faq, and get_ksef_info.

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 for when to use the tool: whenever product/pricing information for these specific products is needed. It does not explicitly mention alternatives or when not to use it, but the focused scope and sibling list make the intended usage evident.

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

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

With no annotations, the description carries the transparency burden. It discloses the retrieval algorithm (BM25), the corpus scope, and that it returns relevant content—useful behavioral context for a read-only search tool. It does not specify result format or caveats, but no destructive behavior is implied.

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, front-loaded sentence that places the action and resource first. Every clause adds useful information—algorithm, corpus, and return behavior—with zero filler.

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 two-parameter search tool with no output schema, the description is largely complete: it identifies the corpus, algorithm, and result type. It could be improved by stating result fields (e.g., title, URL, snippet), but the schema plus description provide sufficient context for correct invocation.

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 documents both parameters fully (query with min/max length and language, topK with min/max/default). The description adds domain context but no additional parameter-level meaning, so it earns the baseline score for high 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?

The description names the specific verb ('search'), the exact resource ('rafsoft.net knowledge base'), and the scope ('FAQ, docs, blog, pricing'). It also states the return behavior ('Returns relevant content') and the domain, making it clearly distinguishable from siblings like get_faq.

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 signals it is for cross-cutting full-text search across multiple knowledge base sections, implying when it should be used. It does not explicitly name alternatives or exclusions, but the context is specific enough for an agent to choose it over the sibling tools.

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