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

With no annotations, the description carries full burden. It mentions false positives are filtered out, returns classified hits with severity and provenance, and accepts advanced fields. It does not mention rate limits or authentication requirements beyond API keys.

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 concise paragraph of 4 sentences, front-loaded with purpose. It could be slightly tighter but avoids fluff.

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 11 optional parameters and no output schema, the description covers the tool's purpose, output structure (categorized hits, severity, provenance), and links to regdata_describe for extended fields. It is sufficiently complete for a screening 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?

Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema's parameter descriptions, which are already detailed.

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 it is an 'Adverse Media Screener - KYC/AML Negative News Check' and explains it screens a person or company for adverse media, returning categorized hits with entity role, severity score, and source provenance. This distinguishes it from sibling tools which are registry lookups.

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 KYC/AML and due diligence but does not explicitly state when to use alternatives or when not to use this tool. The context of sibling names (registry tools) provides indirect differentiation.

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 full burden. It discloses the tool is a scraper but fails to mention rate limits, authentication, caching, or limitations on date range or result counts.

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 (3 sentences) and front-loaded with purpose and key details. Every sentence adds value without redundancy.

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?

All 9 parameters are described in the schema, but no output schema exists. The description lacks details on behavior for edge cases (e.g., large date ranges, pagination) and does not fully compensate for missing annotations.

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%, so baseline is 3. The description does not add extra meaning beyond the schema's parameter descriptions, except noting that advanced fields are available via regdata_describe.

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 an Austria-specific insolvency and court publications scraper, listing specific data types and the source (Ediktsdatei). It distinguishes from sibling tools which target other jurisdictions.

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 states there is no API alternative, implying scraping is necessary. It also references 'regdata_describe' for advanced fields, but does not explicitly state when not to use this tool 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.

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

With no annotations, the description carries the full burden. It clearly identifies the tool as a scraper that reads from a public register, and specifies limitations (no API, no login access). It could explicitly state it is read-only, but the context implies safe, non-destructive use.

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 front-loaded with the key action and resource. It efficiently packs purpose, return data, limitations, and a pointer to additional fields. While slightly verbose, it avoids redundancy and each sentence serves a 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?

For a tool with 4 parameters and no output schema, the description covers purpose, data returned, limitations, and parameter behavior (via schema). It explains pagination in maxResults and provides enough context for an agent to decide when to use it. Lacks explicit description of error cases or empty results.

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%, baseline 3. The description adds value by contextualizing the search query (674,000+ entities by name/NIP) and noting that advanced fields are accepted via regdata_describe. This goes beyond the schema's definitions.

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 precisely states the tool searches the Poland BDO Waste Registry by name or NIP, returning specific fields (BDO number, company name, NIP, address). It explicitly answers the question 'is this company in the BDO register?' distinguishing it from other Polish registry tools listed as siblings.

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 tells when to use (checking BDO registration for ESG/due diligence) and explicitly states what NOT to use it for (waste codes/permits are behind login). It also directs to regdata_describe for advanced fields. However, it does not contrast with specific sibling tools.

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 must cover behavioral traits. It mentions that it scrapes from the official public search, requires no API key, and includes a polite delay parameter. However, it lacks details on rate limits, caching, or whether it modifies data, leaving some gaps.

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, focused paragraph. It front-loads the tool's purpose, lists key data fields, then explains lookup methods and extras. Every sentence contributes information; there is no redundancy.

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 8 parameters with full schema descriptions and no output schema, the description adequately covers purpose, usage, and parameter intent. It could be improved by mentioning the output format or structure, but it lists the types of data returned and references advanced fields.

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?

All 8 parameters have schema descriptions (100% coverage), so the description adds marginal value. It does provide useful context such as phonetic search behavior and the purpose of minIntervalMs, but the schema already covers the basics.

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 scrapes Belgium's KBO/BCE registry and lists specific data fields (identity, status, legal form, address, activity codes, directors). It distinguishes from siblings by specifying Belgium, making it unique among country-specific 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 explains lookup by enterprise/VAT number or company name, and mentions two search modes (byNumber, byName). It references regdata_describe for advanced fields, but does not explicitly state when not to use this tool or provide alternatives.

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 provided, so description carries full burden. Mentions parsing and advanced fields, but does not disclose safety traits (e.g., idempotency, side effects, rate limits) or potential issues like PDF format changes. For a read-like operation, more transparency expected.

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 cover purpose and advanced fields. No fluff, but could be slightly more structured (e.g., separating usage from features). Overall 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?

Explains purpose and parameter usage, but lacks details on output format, result structure, pagination, or error handling. Given no output schema, more completeness would be beneficial. Adequate but not comprehensive.

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% (all parameters described). Description adds minimal value beyond schema: notes advanced fields accepted via regdata_describe. Baseline 3 is appropriate since schema already documents parameters well.

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 parses Spanish BORME Section A PDFs into structured corporate acts, listing specific examples (incorporations, appointments, etc.). This is a specific verb+resource and distinguishes from country-specific sibling 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 that it processes daily PDFs, but no explicit when-to-use or when-not-to-use guidance. Alternatives are implied by sibling tool names (other countries), but no direct comparison or exclusion criteria.

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?

Given no annotations, the description carries full burden. It discloses the 500-match limit, incomplete result reporting, and that no API key is required. However, it omits details on rate limits, error handling, or the format of returned data, leaving some behavioral aspects undisclosed.

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 extremely concise—two sentences plus two brief warnings—with all information front-loaded. No redundant words; every sentence 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?

For a tool with 3 parameters and no output schema, the description covers purpose, input, output fields, key behavioral caveats, authentication needs, and extensibility. It lacks details on return format and error handling, but the provided information is sufficient for an AI agent to use the tool effectively.

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 parameter descriptions. The main description adds value by explaining that searchQueries can include entity numbers, that maxResultsPerQuery has a default of 50 to avoid surprise charges, and that additional parameters beyond the schema are accepted (via regdata_describe). This provides meaningful context beyond the schema alone.

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 a scraper for the California Secretary of State business registry, listing specific data fields (entity name, number, status, etc.) and search methods. This distinguishes it from sibling tools focused on other jurisdictions or registries.

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 explains how to search (by name or entity number) and warns about the 500-match limit, advising users to narrow terms if results are incomplete. It also mentions no API key is needed and points to regdata_describe for advanced fields. However, it does not explicitly contrast with other California-specific siblings (e.g., regdata_california_ucc_lien), slightly reducing guidance on alternatives.

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 full burden. It discloses that the registry rejects searches matching >1000 filings, reports refusal distinctly from 'no liens', and notes that advanced fields beyond schema are accepted. However, it does not explicitly state that the tool is read-only or mention authorization/rate limits.

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, front-loaded with the tool's purpose and resource. Every sentence adds value: purpose, returned data, error handling guidance, and extensibility. No redundancy.

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 sibling tools, the description covers purpose, parameter hints, error behavior, and additional parameter extensibility. It mentions data returned but could explicitly state it returns list results. Overall fairly 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?

Schema coverage is 100%, baseline 3. The description adds significant meaning beyond schema for 'searchQueries' (contains-match, name specificity warning) and 'maxResultsPerQuery' (default 50 to avoid surprise charges, set 0 for no limit). Other parameters like 'filedTo' and 'filedFrom' are adequately explained in schema and description adds little.

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 searches the California Secretary of State UCC filing index by debtor or secured-party name and lists returned data (filing number, type, status, dates, parties). This distinguishes it from sibling tools like regdata_california_sos_business, which searches business entities.

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 does not explicitly state when to use this tool versus alternatives. It provides advice on narrowing broad searches and mentions no API key needed, but lacks explicit context on tool selection among siblings.

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 provided, so description carries full burden. It accurately describes the read-only nature of listing tools and what the output includes (names and return descriptions). No contradictions or hidden 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?

Description is two sentences, but the second sentence is somewhat long. However, it is well-structured, front-loaded with purpose, and each part earns its place. Minor redundancy could be tightened.

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 parameters and no output schema, the description is fully complete. It explains the catalog function, provides practical usage guidance, and covers all needed context for this simple 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?

Tool has zero parameters, so schema coverage is 100%. No parameter descriptions needed; baseline 4 applies since description does not need to add parameter information.

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 lists all getregdata business-registry tools across 16 jurisdictions with tool names and return descriptions. It distinguishes itself from sibling tools by being a catalog, and provides a specific use-case example for Poland.

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?

Explicitly tells when to use this tool (to find the right single-registry tool for other jurisdictions) and when to prefer a sibling (regdata_poland_kyb_check for Polish companies), with a clear rationale for avoiding chaining.

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?

The description adds useful context: 'not found' is a real answer, each lookup is separate, and NIT lookups include branches. With no annotations, more could be expected (e.g., rate limits, cost, safety), so a 3 is appropriate.

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 reasonably concise and front-loaded with the purpose. It could be slightly more compact, but it is well-structured and informative.

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 lack of output schema and the tool's complexity (7 parameters), the description covers the main function, search keys, and return fields. It also mentions additional capabilities via regdata_describe, making it fairly 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?

Schema coverage is 100%, so each parameter already has a description. The main description adds some context (e.g., NIT format tolerance) but does not significantly enhance beyond the schema.

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 it searches the Colombian RUES register by name, NIT, or matricula, returning specific identity and registration fields. This distinguishes it from sibling tools which target other registries.

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 how to use the tool (search by various identifiers) and references regdata_describe for advanced fields. However, it does not provide explicit when-not scenarios or alternatives.

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 must disclose behaviors. It mentions the read-only nature ('Search') and output fields (names, citizenship, control nature, ownership %), and notes a pay-per-result model. However, it does not discuss rate limits, error handling, authentication, or any destructive aspects. The description is adequate but lacks depth on behaviors beyond the basic 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 five sentences, each adding value. It front-loads the tool's purpose ('Poland CRBR Beneficial Owners Scraper'), then covers functionality, output, pricing, complementary tools, and extensibility. No wasted words; efficient and informative.

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 covers the return fields well. It mentions the tool's role in KYC/AML, paired checks, and extensibility. It lacks details on pagination, error handling, or typical response size, but overall provides a solid understanding of the tool's capabilities and context among many sibling 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 coverage is 100%, so baseline is 3. The description mentions searching by NIP or KRS, which aligns with schema parameters, but does not add significant meaning beyond the schema for other parameters like dateFrom, dateTo, maxItems, companyName, or queries. The mention of 'Advanced fields... regdata_describe lists them' provides some extra context but does not clarify individual parameter semantics.

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 searches the Polish CRBR beneficial owner registry by NIP or KRS and returns structured UBO data (names, citizenship, control nature, ownership %). It distinguishes itself from siblings like regdata_polish_premises, regdata_polish_regon, and regdata_poland_kyb_check by focusing on beneficial owners for KYC/AML onboarding.

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 context for when to use: for Polish beneficial owner verification in KYC/AML onboarding. It mentions complementary tools (debtor and KNF checks) and notes it's pay-per-result with no subscription. However, it does not explicitly state when not to use or name alternative tools for similar purposes.

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 provided, the description carries the full burden. It adds behavioral context by stating that a 'not found in the register' is a valid response, not an error, and that advanced fields are accepted. However, it does not cover other important behaviors like rate limits, authentication, or idempotency.

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 highly concise: three sentences with no redundancy. It front-loads the purpose, then details returns, then adds a key behavioral note, and finally hints at advanced usage. Every sentence 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?

Given the tool has 6 parameters, no required fields, additional properties, and no output schema, the description is relatively complete. It explains the search scope, output fields, a critical behavioral nuance, and where to find extended parameters. Missing are examples or guidance on constructing queries, but overall 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 100% description coverage, so the baseline is 3. The description adds value by mentioning that advanced fields beyond the schema are accepted and pointing to 'regdata_describe' for a list, but does not elaborate on the existing parameters beyond what the schema already provides.

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 scrapes the Cyprus DRCOR register, specifies it looks up companies by name or HE number, and lists what it returns (identity, status, directors, secretary, registered office). This distinguishes it from the many other country-specific registry siblings.

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 Cyprus companies through the name and context, but does not explicitly mention when to use this tool versus alternatives like other country registries or when not to use it. It provides no guidance on exclusions or alternative tools.

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 provided, the description carries full burden for behavioral disclosure. It reveals that the register refuses broad queries outright, that refusals are reported distinctly from 'no records,' and explains the impact of includeEnded on refusal likelihood. It also notes acceptance of advanced fields and no API key requirement, though it does not detail rate limits or error handling.

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 relatively concise for the amount of information conveyed, front-loading the main purpose and then covering limitations and tips. It could be slightly more structured but remains readable and 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?

Given no output schema, the description lists key return fields and warns about refusal behavior. It covers important usage nuances (includeEnded, watchlist, maxResults) and mentions advanced fields via regdata_describe. It does not detail the return format or error handling, but is fairly complete for a data search 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?

Schema description coverage is 100%, but the description adds significant value beyond the schema by clarifying behavioral aspects: includeEnded's default effect, watchlist overriding 'name' parameter, and maxResults capping export. It also mentions additional accepted properties.

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 searches the Czech ISIR Insolvency Register by company name, debtor name, IČO, or city, and lists returned fields (debtor name, IČO, case reference, court, status, cross-link). It differentiates from sibling tools (various country-specific registers) by specifying jurisdiction and function.

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 practical guidance on query limits (400 rows), behavior of includeEnded flag (default true causing potential refusals), and suggests narrowing with IČO/city or setting includeEnded false. It does not explicitly state when not to use, but the limitations are well explained.

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 provided, description carries full burden. Discloses read-only fetch behavior, no side effects mentioned. Simple operation, so transparency is adequate.

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, no wasted words. Front-loads purpose and usage. Every sentence serves a clear function.

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?

No output schema, but description mentions returning 'live input schema + description'. For a simple schema-fetcher, this is sufficient context. Could specify output format more, 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?

Input schema has 100% coverage with descriptions. Tool description adds explicit guidance that either slug or tool should be provided (mutual exclusivity), which schema does not state. Adds value beyond schema.

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 it fetches the live input schema and description for an actor, distinguishing it from sibling data-fetching tools. Verb 'Fetch' and specific resource 'live input schema + description for one actor' provide precise 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?

Implies usage before calling other tools to construct correct calls, and instructs to provide either slug or tool name. Lacks explicit when-not or alternatives, but sufficient given meta-tool nature.

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?

Given no annotations, the description covers key behaviors: authentication (keyless), cost model (pay per result), free plan allowance, and auto-calculation of check digit in kwNumbers. Does not disclose error handling for invalid entries or response format details.

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?

Description is reasonably concise and front-loaded with core purpose. Includes necessary details like parameter clarifications and plan limits, though some information (e.g., free plan note) is placed inside the kwNumbers description rather than in a separate section.

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, parameters, and some usage context. However, without an output schema, the description does not explain return values or output structure. Missing details on error handling or what happens on invalid inputs, which are important for a scraping 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?

Schema coverage is 100%, but the description adds value beyond schema descriptions: explains kwNumber format with auto-calc, describes viewType enum meanings, default sections, and maxConcurrency behavior. Provides context missing from schema (e.g., free plan limit).

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 a scraper for Poland's Electronic Land Registry (EKW), specifying the extracted data types (ownership, mortgages, restrictions, property details) and the context (property due diligence, credit-risk checks). It differentiates from numerous sibling tools focused on other countries or registries.

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?

States the tool is keyless and for property due diligence/credit-risk checks, with pay-per-result pricing. Mentions free plan limits and upgrade for unlimited batches. Hints at an alternative for advanced fields (regdata_describe) but does not explicitly compare with other siblings or provide when-not-to-use scenarios.

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 provided, the description carries full burden. It mentions 'No official API' and that advanced fields are accepted via regdata_describe, but lacks details on rate limits, pagination, data freshness, or error behavior.

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?

Description is relatively concise and front-loaded with purpose. It efficiently covers key points without excessive verbosity, though it could be slightly tighter.

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?

No output schema; description partially covers return values by listing extracted fields. However, it lacks details on pagination, error handling, and usage limits, making it adequate but not comprehensive.

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%, so parameters are well-documented in the schema. The description adds context about advanced fields being accepted, but does not elaborate on parameter usage beyond what the schema provides.

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 searches the German Commercial Register and extracts specific data like officers and capital. However, it does not explicitly differentiate from sibling tools like regdata_austria_ediktsdatei, relying on the tool name for country distinction.

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. No when-not scenarios or prerequisites mentioned. The description only states what the tool does, not when it's appropriate.

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 full burden. It discloses that the register returns 'too many matches' instead of results (differentiating from 'no insolvencies'), and notes that advanced fields beyond the schema are accepted. However, it omits details on data freshness, rate limits, or response 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 a single coherent paragraph that is front-loaded with purpose. It is clear and not excessively long, though it could benefit from bullet points or subheadings for readability.

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 11 parameters and no output schema, the description covers key edge cases (too many matches, watchlist usage, default dates) and mentions additional accepted fields. It lacks explicit return structure details but is largely 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?

Schema coverage is 100%, yet the description adds significant value: explains 'name' as contains-search, 'state' filter helps avoid cap, 'dateFrom' defaults, 'exactMatch' behavior, and 'watchlist' for bulk monitoring. This goes well beyond the schema alone.

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 a search of Germany's official insolvency register by debtor name, listing specific returned fields (court, case number, etc.). It is distinct from sibling tools like regdata_czech_isir_insolvency or regdata_spain_concursal.

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 explains when to use the tool (for German insolvency searches) and provides guidance on handling the 'too many matches' scenario by narrowing queries. It also notes that no API key is required. However, it does not explicitly contrast with sibling tools for similar purposes.

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 full burden. It discloses that directors/shareholders are not included (paid documents) and that 'not found' is an answer. It lacks details on rate limits or auth, but for a registry scraper this is adequate.

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 redundant information. It front-loads the purpose and covers key details in two sentences plus a few clarifications. Every sentence 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?

Given no output schema, the description fully explains what is returned (identity, type, status, date, address, email) and what is not (directors/shareholders). It also notes advanced fields via regdata_describe. Complete for a registry lookup.

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%, so baseline is 3. The description adds context like 'A broad name can hit the register's 50-result ceiling' but does not significantly go beyond schema descriptions. Adequate.

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 it is an 'Ireland CRO Company Registry Scraper' that looks up Irish companies by name or CRO number, and lists return fields. It distinguishes from sibling tools by specifying the Irish CRO register, which is unique among the siblings.

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 guidance on advanced fields via regdata_describe and clarifies that 'not found' is a valid answer. However, it does not explicitly compare with sibling tools or 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.

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

Without annotations, description adds value by disclosing 'No official API' and that advanced fields are accepted. It doesn't discuss side effects, but as a read-like lookup, the transparency is adequate.

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?

Three concise sentences: first defines purpose, second lists use cases, third notes schema extensibility. No wasted words, front-loaded with key info.

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?

No output schema, but description specifies key outputs (PEC email, SDI code) and result structure. It also mentions advanced fields via another tool. Sufficient for a lookup 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?

With 100% schema coverage, description still adds meaning: clarifies identifier types (VAT, CF, name) and output behavior ('One result row per identifier'). It also references 'regdata_describe' for advanced fields, enriching schema info.

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 resolves Italian PEC email and SDI code from VAT, tax code, or company name. It distinguishes from siblings like 'regdata_italy_registro_imprese' by focusing on PEC/SDI lookups.

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?

Description mentions use cases: e-invoicing, KYC, bulk enrichment. It implies when to use (for Italian company digital data) but does not explicitly state when not to use or suggest alternatives.

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 present, so the description bears full responsibility. It discloses that the tool is a scraper without an official API ('No official API - structured JSON') and mentions that advanced fields are accepted, but it omits details on rate limits, error handling, data freshness, or what happens when a company is not found.

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 an additional note, all front-loaded with the core purpose. No extraneous words; every sentence adds necessary context.

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 a scraper with no output schema or annotations. The description lists returned fields and indicates JSON output, but does not specify response structure, pagination, or error behavior. For a simple data retrieval tool, it covers the essentials but could be more 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?

Schema description coverage is 100%, so the schema itself documents all parameters. The description adds value by noting that 'Advanced fields beyond this schema are also accepted (regdata_describe lists them)', but does not elaborate on param usage beyond the schema.

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 the tool is an Italy Registro Imprese scraper, specifies the search method (by company name), and lists the data fields extracted (name, registered office, PEC, legal form, ATECO, business description). It clearly distinguishes itself from sibling tools through the name and registry specificity.

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 when an Italian company name or VAT is available, but does not explicitly state when to use this tool versus alternatives like regdata_italy_pec or other country-specific tools. No guidance on when not to use it, prerequisites, or limitations is provided.

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 must convey behavioral traits. It states 'No auth needed' and implies a read-only operation ('extract', 'search', 'export'). It mentions that additional fields are accepted, but does not disclose potential behaviors like rate limits, data freshness, or whether it modifies data. More explicit safety info would raise the score.

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 only 6 sentences that front-load the tool's purpose and key capabilities. Every sentence provides useful information without redundancy. It efficiently covers search, export, format, and advanced options.

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 has 7 parameters and no output schema or annotations. The description covers essential aspects: purpose, search methods, export capability, output format (JSON), and a pointer to extended features. Missing details include pagination behavior, result size limits beyond maxResults, and potential performance considerations. However, for a scraper tool, it is fairly 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?

Schema description coverage is 100%, so the schema already provides detailed parameter descriptions. The description adds context like 'Bulk export 75,000+ entities' and points to regdata_describe for advanced fields, which adds value but does not significantly elaborate on the existing schema definitions. Baseline 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 clearly identifies the tool as a 'Poland KNF Financial Registry Scraper' and specifies the types of entities it extracts (payment institutions, e-money issuers, credit intermediaries, etc.) and search methods (name, NIP, entity type). It distinguishes itself from sibling tools, which are for other registries/countries, by focusing on Polish KNF registries.

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 guidance on when to use the tool: for searching Polish KNF registries, bulk export, and using structured JSON. It also mentions that advanced fields are available via regdata_describe. However, it lacks explicit instructions on when not to use this tool (e.g., for other countries) or alternatives, though this is implied by the sibling context.

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?

Describes that the tool downloads public PDF extracts and extracts names, roles, and company metadata. No annotations to contradict. Discloses it performs file retrieval/parsing beyond a simple API call, but lacks details on rate limits or auth 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: first states purpose, second explains why this tool is needed (official API censors). Efficient and front-loaded with key info. Could be slightly more structured but overall well-sized.

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?

No output schema, but description covers return contents: 'complete names, roles, and company metadata'. Mentions advanced fields beyond schema. Adequately complete for a tool with 3 params and no nested objects.

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%, but description adds meaning: 'Each number will be padded to 10 digits automatically' for krsNumbers, and explains 'aktualny' vs 'pelny' extract types. Adds value beyond schema descriptions.

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?

Specific verb+resource: 'Extract full non-anonymized board member & shareholder names from Polish KRS'. Distinguishes from siblings like regdata_poland_krs_financial by focusing on board/shareholder data vs financial data, and notes it bypasses official API censorship.

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?

Clear context: use when you need complete names censored by official API. Mentions advanced fields via regdata_describe. No explicit when-not or alternatives for other Polish tools, but sibling differentiation is implied.

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?

Despite no annotations, the description discloses pay-per-result model, session caching behavior (in parameter description), that proceedings are resolved automatically, and the meaning of estateInventoryPublished:false. It could mention error handling or rate limits, but overall provides substantial behavioral insight.

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 paragraph that front-loads the main purpose and then details key features. It is informative without excessive verbosity, though slightly dense.

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 complexity (11 params, no output schema), the description covers essential aspects: search modes, core parameter usage, and a key mode's output. It omits full output structure for all modes, but the provided detail suffices for effective use.

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%, baseline 3. Description adds value by explaining mode semantics (e.g., bankruptcyEstate specifics) and how entityName/identifier interact. This enhances understanding beyond the schema's short descriptions.

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 identifies the tool as a scraper for Poland KRZ National Debtor Registry, specifying it automates bankruptcy and restructuring checks. It lists nine search modes, making the purpose distinct from sibling tools that cover other registries.

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?

Description provides use cases (distressed-asset sourcing, creditor recovery, credit-risk watchlist, insolvency workflow) and directs to regdata_describe for advanced fields. It does not explicitly contrast with siblings, but the unique registry focus makes usage context clear.

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 provided, yet description omits critical behavioral traits such as authentication, rate limits, pagination, or error handling. Only mentions fetchDetails slows down but returns complete content. Significant gaps for a parameter-heavy tool.

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?

Four sentences, front-loaded with purpose. Could be slightly more structured, but no fluff. The reference to regdata_describe is appropriate but adds a minor complexity.

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?

Lacks explanation of output structure (JSON format unspecified) and how date range works. No details on search type differences. With 12 parameters and no output schema, more context would help agent select correctly.

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%, so baseline holds. Description adds little beyond schema: mentions full-text search character limits and partial matching, but already present. The note about accepting advanced fields via regdata_describe is helpful but minor.

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 searches Polish Court & Economic Gazette for bankruptcy, liquidation, restructuring, and creditor calls. Distinguishes from siblings by specifying 'Poland MSiG' and referencing archive from 2001.

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?

Implies usage for Polish economic/legal announcements but lacks explicit when-to-use or comparisons with related tools like regdata_czech_isir_insolvency. No guidance on when not to use.

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 fully discloses behavioral traits: name-similarity matching, partial name support, the register's 50-result ceiling, exact registration-number lookups, and the effect of nameMatchMode. It also explains that broad terms hit the ceiling and how to reach beyond it.

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 moderately long but well-organized with clear sentences. It could be slightly more concise, but the level of detail is justified given the tool's complexity. No wasted 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 lack of an output schema, the description covers the essential context: input methods, behavior, limitations (50-result ceiling), and the significance of 'not found'. It is comprehensive for a tool with multiple parameters and search modes.

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%, but the description adds significant value beyond the schema. For example, it explains how nameMatchMode affects results (including the 'LIMITED' example), why leaving statusFilter empty is safer for screening, and the format flexibility of registration numbers. This extra context aids correct parameter 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 identifies the tool as a scraper for the Nigerian CAC registry, specifies search by entity name or registration number, and lists the returned fields (registered name, registration number, etc.). It is distinct from sibling tools which target other registries.

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 explains when to use the tool (to search the Nigerian CAC register) and mentions advanced fields available via regdata_describe. It also clarifies that a 'not found' result is valid. However, it does not explicitly contrast with sibling tools or 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.

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

No annotations provided. Description discloses parsing of XML/XHTML/iXBRL, pay-per-result model, and that files over 5MB are linked via key-value store. Also explains that statements larger than maxStatementSizeMB are skipped without charge. No contradictions with annotations.

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 paragraphs. First sentence states purpose immediately. Every sentence adds value, covering format, use cases, pricing, and additional features. No redundancy.

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?

No output schema; description lists key output fields (balance sheets, income statements, etc.) and formats. Lacks full output structure but provides sufficient context for a data extraction tool. Complexity is moderate and description is adequate without exhaustively detailing all return fields.

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 descriptions for all 5 parameters. Description does not add new details about these parameters but mentions advanced fields beyond schema. Baseline score of 3 is appropriate as description provides minimal added parameter context.

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 it extracts structured financial statements (balance sheets, income statements, assets, equity, revenue, net profit) from Polish KRS filings. It specifies the source and output format, distinguishing it from sibling tools that cover other registries or data types.

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?

Explicitly recommends use in credit-risk or M&A due-diligence workflows. Mentions pay-per-result and that advanced fields beyond schema are listed via regdata_describe. Provides context but lacks explicit when-not-to-use statements.

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 provided, the description fully discloses behavioral traits: expected timing (20-30 seconds, up to 2.5 minutes), timeout behavior (returns status/runId/note), billing rules (partial verdict not billed, never retry), and that advanced fields are accepted. This level of transparency is critical for an agent to invoke the tool correctly.

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 comprehensive and front-loaded with the core purpose, but it is relatively long. Each sentence adds value, though some redundancy could be trimmed (e.g., 'one call, one verdict' and 'COMPLETE' emphasis). A slightly tighter version would be more efficient, but it remains clear and well-organized.

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 complexity (multiple registries, timeout handling, billing) and the absence of an output schema, the description covers all necessary aspects: purpose, parameters, behavior, return values (verdict types and timeout result), and error handling. It is complete for an agent to use the tool effectively.

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?

Although schema coverage is 100%, the description adds meaningful context beyond the schema. For 'identifiers', it explains the format (NIP or KRS, 10 digits, leading zeros) and what each identifier returns. For 'maxItems' and 'krzConcurrency', it clarifies the purpose and default behavior. This helps the agent choose appropriate values.

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 performs a complete Polish KYB check using a single NIP or KRS, distinguishing it from sibling tools that handle individual registries or other jurisdictions. It specifies the verb 'Run a COMPLETE Polish KYB check' and the resource 'from a single NIP or KRS', making 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?

The description provides explicit guidance on when to use this tool ('start here for Poland instead of chaining the individual registry tools') and when not to ('use the per-registry tools for other jurisdictions or for deeper single-source detail'). It also warns about billing and timeout behavior ('Never re-run the check to retry: that bills twice'), which helps the agent avoid costly mistakes.

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 provided, the description must disclose behavioral traits. It mentions 'Official Sejm API source' but does not cover rate limits, authentication, or side effects. The tool is likely read-only, but this is not explicitly stated, leaving gaps for the agent.

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 concise sentences: the first states the tool's purpose, the second elaborates on the dataset, and the third points to additional fields. No redundant information, every sentence contributes.

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 5 optional parameters and no output schema, the description adequately states the tool's purpose and data contents. However, it lacks details on output format or structure, and the additionalProperties flag implies flexibility that is only hinted at. Overall, it meets minimum completeness but not exceptional.

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?

All 5 parameters are fully described in the schema (100% coverage), meeting the baseline. The description adds value by noting that advanced fields beyond the schema are accepted and directing to regdata_describe for the full list, which aids parameter usage beyond schema definitions.

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 it is a Poland Parliamentary PEP scraper for Sejm members, listing specific fields (names, DOB, party, district, profession) and the use case (KYC/AML). It distinguishes itself from sibling tools that cover other Polish registries or data types.

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 the tool is for KYC/AML screening but does not explicitly state when to use it versus alternatives like regdata_polish_premises or regdata_polish_regon. The mention of regdata_describe for advanced fields is helpful but not a usage guideline.

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 provided, so the description carries the full burden. It discloses key behavioral traits: the paid LinkedIn budget (maxEnrich), rate limiting (minIntervalMs), headcount estimation methods (including limitations like ±50-100% for industrial sites), and that the footprint estimator uses free APIs not counted against budget. It also notes that large offices are not cleanly identifiable. It does not explicitly state read-only or destructive nature, but the context implies data retrieval.

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 relatively long but well-structured. It front-loads the purpose and then covers key parameter behaviors. Each sentence adds value, though some details (like the explanation of headcount methods) could be slightly condensed. Overall, it is well-organized and informative without being overly verbose.

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 19 parameters and no output schema, the description provides a high-level output description ('One row per premise ... with industry, address, company age, and ownership') and mentions that advanced fields are documented elsewhere. It also leverages sibling context (many Polish regdata tools) to position itself. While output details are not exhaustive, the description adequately covers the tool's functionality.

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%, so baseline is 3. The description adds substantial meaning beyond the schema: e.g., for 'maxEnrich' it explains cost per call and that the footprint estimator is free; for 'headcountMethod' it explains how 'auto' chooses; for 'minIntervalMs' it gives rate limit rationale. This extra context greatly aids parameter understanding.

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 purpose: 'Build prospect lists at the physical-site level from the Polish REGON registry.' It specifies the resource (premises/jednostki lokalne), the action (build lists), and key features (filtering by region, industry, etc.). The title and content distinguish it from sibling tools like regdata_polish_regon (company-level).

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 context on when to use this tool (for physical-site level prospects) and mentions that advanced fields are documented in regdata_describe. However, it does not explicitly contrast it with regdata_polish_regon or other siblings, nor does it state when not to use it. The guidance is clear but lacks explicit exclusions.

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?

The description discloses important behaviors: no API key required, anonymous scraping, acceptance of additional properties, and delay recommendations (minIntervalMs) to avoid CAPTCHAs. It references regdata_describe for advanced fields. However, it does not detail response format or error handling.

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 focused paragraph that conveys key information efficiently. It is front-loaded with the tool's name and core feature (no API key). Minor improvement could be structuring with bullet points, but it is not verbose.

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 the main use cases, anonymity, and the possibility to fetch local units. It references regdata_describe for additional fields. Given the lack of output schema, it provides adequate context, though it could mention common error states.

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%, so baseline is 3. The description does not add further meaning to parameters beyond what is in the schema. It mentions extensibility via additional properties but does not elaborate on any specific parameters.

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 it is a Polish REGON scraper for the GUS Business Registry, explaining it can look up by NIP/REGON/KRS, discover companies by address and PKD, and fetch local units. This distinguishes it from sibling tools which target other countries or specific registries.

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 does not explicitly state when to use this tool over alternative Poland-specific tools like regdata_poland_kyb_check or regdata_poland_krs_financial. It implies use for REGON scraping but lacks comparative guidance.

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 provided, the description carries the full burden. It discloses that the tool retrieves published acts with date, entity, municipality, and type, and warns that it is an act register, not a company profile. It also notes the 200-result ceiling implicitly via parameter descriptions. However, it does not detail pagination, rate limits, or error handling, which would enhance transparency.

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 front-loaded with the main purpose and then adds relevant details. It is slightly longer than necessary but each sentence adds value, such as clarifying the register type and limitation on results. The structure is clear and logical.

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 lack of an output schema, the description adequately lists return fields (date, entity, municipality, type) and mentions additional fields via regdata_describe. It also discusses the 200-result ceiling and how to work around it. For a scraper tool with 8 optional parameters, the description provides sufficient context for an agent to use it effectively.

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%, so the baseline is 3. The description adds value beyond schema descriptions by explaining that NIF is preferred, name search is broader and may hit the 200-result ceiling, and that date ranges or district can narrow searches. These insights help agents choose and combine parameters effectively.

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 'Search', the specific resource 'official Portuguese Ministry of Justice register of published corporate acts', and the search criteria 'by NIF or entity name'. It explicitly distinguishes this tool as an act/event register, not a company profile, and the name 'regdata_portugal_corporate_acts' inherently differentiates it from country-specific siblings.

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 when to use the tool (searching corporate acts by NIF or entity name) and notes that 'No acts found' is a valid response, guiding agents to handle empty results. It also references regdata_describe for advanced fields. However, it does not explicitly state when not to use it or compare with other country-specific tools, though the tool name and context make this intuitive.

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 provided, but description fully discloses behavior: returns dataset items when finished, returns same handle if still running, mentions billing implications and waitSecs policy.

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?

Three well-structured sentences with no wasted words. Front-loaded with purpose, each sentence adds critical information for agent use.

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, description explains return behavior ('dataset items' or 'same handle') adequately. Parameter descriptions are complete. Could mention status codes or error handling, but sufficient for tool use.

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%, so baseline is 3. Description adds context: runId is from a pending call, maxItems is optional cap, waitSecs includes range and default. This adds value beyond schema descriptions.

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 it collects the result of a running regdata check, specifying verb ('collect') and resource ('result of a regdata check'). It distinguishes from siblings by explaining the polling pattern and avoiding double-charging.

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?

Explicitly tells when to use: when any regdata tool returns {status, runId, note} instead of results. Warns not to re-run the check to avoid double charge, and instructs to call with the runId.

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 mentions the tool is a scraper and returns certain data, but lacks details on error handling, rate limits, or authentication needs.

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 two sentences, front-loading the core purpose and key details without unnecessary fluff.

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 that there is no output schema and no annotations, the description is reasonably complete. It explains the tool's function, return data, and mentions additional fields, though it could detail output structure more.

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 clear parameter descriptions. The tool description adds minimal extra meaning beyond the schema, such as noting advanced fields available via regdata_describe.

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 searches Slovakia's beneficial-ownership register by IČO or company name, returning beneficial owners with PEP flag and authorised person. This distinguishes it from sibling tools focused on other countries.

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 Slovakia's register but does not explicitly state when to use this tool over alternatives or when not to use it. The 'No API key' note provides minor guidance.

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 must carry the full burden. It discloses that the tool scrapes a free source with no API, but does not mention rate limits, data freshness, or potential scraping failures. The maxConcurrency parameter implies parallel scraping, but behavioral constraints like throttling are not addressed.

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, each contributing unique information: the tool's purpose and data types, its source and richness, and extensibility via advanced fields. No wasted words, and the key 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?

Given 8 parameters with no required ones, full schema coverage, and no output schema, the description covers the tool's data sources and capabilities well. However, it does not describe the output format or provide an example, which would enhance completeness for an agent.

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%, so parameters are already well-documented. The description adds value by noting that advanced fields beyond the schema are accepted and listed by regdata_describe, but this is a minor addition. Baseline 3 is appropriate as the schema does the heavy lifting.

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 scrapes French company data from Societe.com, lists specific data types (directors, financials, shareholders, etc.), and distinguishes it from other country-specific sibling tools. It also notes it's the 'richest free French company data source with no API', reinforcing its unique 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 description implies usage for French company data but does not explicitly state when to use this tool versus alternatives (e.g., when real-time data is needed or for specific jurisdictions). It lacks exclusions or when-not-to-use guidance, though the mention of advanced fields hints at consulting regdata_describe for extended capabilities.

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 provided, the description must disclose behavioral traits. It indicates the tool is a scraper but does not mention read-only status, rate limits, data freshness, or potential side effects. The description is too minimal for a scraping tool.

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 (3 sentences) with no superfluous content. It front-loads the tool name and purpose, followed by acceptable parameters and a reference to another tool. Every sentence 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?

The description lists data fields returned but lacks details on the output format or pagination behavior. Given no output schema and 7 parameters, more detail on how results are structured would improve completeness, though the reference to regdata_describe partially compensates.

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?

Input schema coverage is 100% with each parameter described. The description adds value by noting that advanced fields beyond the schema are accepted and directing users to regdata_describe for details, which enhances understanding beyond the schema.

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 tool's purpose: scraping Spain's official company directory (Registro Mercantil) and listing the specific data fields (NIF, officers, CNAE codes, etc.). It differentiates from siblings by targeting Spain and referencing regdata_describe for advanced fields.

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 mentions that no API exists for this data, implying this tool is the only means, but it does not explicitly state when to use this tool over alternatives like regdata_spain_concursal or other country-specific tools. No exclusion criteria or alternatives are given.

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. It explains the tool's purpose, mentions no API key requirement, and notes that advanced fields are accepted. It does not disclose rate limits, pagination details (though hinted in maxResults schema), or any other behavioral traits.

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 four sentences, front-loading the purpose and key details without any wasted words. It efficiently communicates the tool's function and notes.

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 provides a useful high-level overview of the return type (matching parties with roles). It is complete enough for a search tool, though additional detail on the output structure would be beneficial.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description adds no additional meaning to individual parameters beyond the schema, but it does mention that advanced fields are accepted.

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 searches Spain's official public insolvency register, specifying the registry name and the types of searches (by debtor name or NIF). It also distinguishes from siblings by country and register type.

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 the tool is for Spanish insolvency searches and mentions alternative advanced fields via regdata_describe, but does not explicitly state when to use or not use this tool versus sibling tools.

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 provided, so description bears full burden. 'Scrape' implies read-only, but does not explicitly state non-destructive behavior, rate limits, or error handling. Acceptable but minimal disclosure.

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?

Three sentences, no fluff. Front-loaded with purpose and scope. Every sentence adds value. Excellent conciseness.

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?

No output schema, but description lists returned fields and search methods. Mentions regdata_describe for deeper schema. Covers key aspects for a scraper tool. Could add notes on data freshness or pagination behavior, but overall sufficient.

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%, baseline 3. Description adds value by stating search by name/identifier/trade name, but this mostly mirrors schema. Cross-reference to regdata_describe for advanced fields is helpful but minor.

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 verb 'Scrape', specific resource 'UAE ADGM public register', and lists fields (company name, registration number, etc.). Name and context distinguish it from sibling tools covering other countries/registers.

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?

Implied usage for ADGM register, but no explicit when/when-not guidance. Mentions 'No API key' as prerequisite and cross-references regdata_describe for advanced fields, but does not clarify when to use this tool over other regdata_* tools.

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?

Without annotations, the description carries the full burden. It discloses that no API exists (indicating scraping), mentions JSON output, and points to advanced fields via regdata_describe. However, it lacks details on rate limits, pagination, or potential delays inherent to web scraping, leaving gaps in behavioral understanding.

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 front-loaded with the essential purpose, followed by supporting details (use cases, output format, advanced field reference). Every sentence adds value with no redundancy or 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?

Given no output schema and no annotations, the description covers core aspects: purpose, parameters (via schema), output format (JSON), and extensibility. It references regdata_describe for additional fields, but lacks specification of default behavior (e.g., ordering of results) or error handling. Still, it is largely complete for a search 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?

Despite 100% schema description coverage, the description adds value by summarizing search dimensions (defendant, industry, legal category) and noting that additional fields beyond the schema are accepted, which goes beyond the parameter docs and helps users understand flexibility.

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 a scraper for the Poland UOKiK registry of abusive clauses, specifying the resource (7,500+ banned contract clauses) and actions (search, scrape) along with use cases (legal compliance, consumer protection). It effectively distinguishes from siblings by naming the specific registry and country.

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 for searching banned clauses but does not explicitly state when to use this tool over alternatives from the extensive sibling list. No when-not guidance or explicit comparison to similar tools is provided.

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 full burden. It describes the tool as a scraper, indicates it makes multiple requests via paging, and notes that advanced fields are accepted via regdata_describe. However, it doesn't disclose rate limits, billing implications (despite mentioning 'billed dataset items'), or idempotency, leaving gaps for an agent.

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 a clear introductory sentence, followed by details on what is scraped and notes about advanced fields. Parameter descriptions are detailed. Slightly could be improved with more structured formatting, but overall 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?

Given no output schema, the description explains what data is returned (names, addresses, etc.) and refers to regdata_describe for advanced fields. It covers all 6 parameters with descriptions. However, it does not describe the response format or error handling, which would be helpful for a scraper 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?

Schema coverage is 100%, yet the description adds significant value by explaining relationships between parameters (e.g., bezirk needs searchQuery, branche synonym), providing examples, and describing paging behavior for maxResults. This goes well beyond the schema.

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 it is a scraper for Austrian businesses from the official WKO directory, listing the data fields obtained. It distinguishes itself from sibling tools by specifying the country and source, making it easy for an agent to select the correct tool for Austrian business data.

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 for Austrian business data and mentions that no API exists, indicating necessity. It provides specific guidance on parameter usage (e.g., bezirk needs searchQuery, branche synonym). However, it lacks explicit when-to-use vs. alternative tools or exclusions, though the context makes it clear.

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