agentdata-nl

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

With no annotations, the description fully carries the burden. It discloses on-chain verification steps, sanctions screening, and that the returned attestation is Ed25519-signed and verifiable offline. It also clarifies limitations: 'Facts and proof, never a guarantee, custody or compliance verdict' and that names are hashed.

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, moderately long paragraph that front-loads the purpose. While concise, it could benefit from bullet points or clearer separation of the multiple steps. Every sentence adds value, but structure could improve scannability.

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 complexity, nested parameters, no output schema, and no annotations, the description covers the main workflow, inputs, and output type. It could be more explicit about the attestation's contents, but overall it provides sufficient context for an agent to understand and invoke the tool 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 40%, but the description enriches each parameter with context: e.g., payer.wallet is 'for on-chain history', payment.tx_hash comes from a 'PAYMENT-RESPONSE', counterparty_name is 'screened against EU/UN sanctions, stored only as SHA-256'. This compensates well for low schema coverage.

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

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

The description clearly states the tool's purpose: for a seller receiving an agent payment to verify settlement on-chain, report payer history, screen against sanctions, and return a signed attestation. It distinguishes from sibling tools like screen_eu_sanctions by combining multiple checks and producing an attestation.

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 opening phrase 'For a seller receiving an agent payment' provides clear context for when to use the tool. It does not explicitly list alternatives or exclusions, but the unique combination of actions implies its specific use case.

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 exist, so description carries full burden. It fully discloses pay-on-code-arrival, dynamic pricing via x402, no account/KYC, and restriction to lawful one-time verification. No 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?

Three well-structured sentences: purpose, payment model, workflow. No redundant words; every sentence adds essential information. Front-loaded with main use case.

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 lack of output schema and annotations, description covers all necessary aspects: objective, input parameters, payment, post-acquisition polling, and usage constraints. Agent can fully understand and execute the 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% with descriptions, but description adds value: explains 'country' defaults to server config, 'operator' defaults to 'any', and gives example values for 'service'. Slightly more context than 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?

Description clearly states the tool obtains a phone number for SMS verification for AI agents, specifying supported platforms and distinguishing from sibling tools like get_phone_code. The verb 'buy' and resource 'phone number' are explicit.

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 step-by-step guidance: supply service and optional parameters, get number and handle, then poll POST /agent/phone-code until code arrives. Also explains payment model and lawful use, enabling correct invocation.

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 lookup (read-only) without stating it explicitly, but lists detailed return fields and mentions it is for companies only. This is sufficient for a safe, non-destructive operation, though an explicit read-only claim would improve clarity.

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 three well-structured sentences. It starts with the tool's purpose, then clarifies parameter usage, and finally lists the outputs. No unnecessary words or repetition.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description fully covers what the agent needs: how to use parameters, what data is returned, and a direct link to the cantonal register excerpt. It matches the complexity of the tool and its sibling set.

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 extra context: an example format for UID (e.g., CHE-105.909.036) and the maximum number of candidates for name search (5). This 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 is for Swiss company checks from the Zefix register. It states the exact capabilities: lookup by UID (exact) or name search (up to 5 candidates), and lists the returned data fields. This distinguishes it from sibling tools targeting 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 provides clear instructions for use (UID or name search) and implies the context (Swiss companies). However, it does not explicitly state when not to use this tool or mention alternatives, though sibling tool names suggest country-specific 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?

No annotations provided, so the description carries the full burden. It discloses that the tool checks two sources (MiCA register and warning list) and returns specific details with references. Behavioral traits like authentication or rate limits are not mentioned, but the description is sufficient for a read-only check 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 extremely concise, using three sentences to convey the core purpose, search methods, and output details. Every sentence provides essential information, with no redundancy or 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 the lack of an output schema, the description adequately explains what is returned (authorisation details and warning-list entries with source references). It covers the key aspects needed for an agent to understand the tool's functionality, though it could mention error handling or fallback behaviour.

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% and the schema already describes each parameter clearly. The description adds only minor context ('Search by firm name, platform website/domain, or LEI' and 'Firms only') which is already implied by the schema descriptions. Thus the description does not add significant value 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 the tool's purpose: verifying if a crypto firm is authorised in the EU by checking two official registers. It specifies the verb 'check' and the resource 'crypto firm authorisation', and distinguishes from sibling tools which address entirely different domains.

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 the tool ('Counterparty/scam check before sending funds') and how to query (by name, website, or LEI). It does not explicitly mention when not to use or list alternatives, but the context is clear and the sibling tools are unrelated, so higher scores are not warranted.

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 data source and returned fields but does not mention rate limits, authentication, or error handling. Behavior is adequately described for a read-only lookup, but not comprehensive.

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

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

The description is concise at three sentences, front-loading the source and purpose. Every sentence conveys essential information without redundancy, making it efficient.

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

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

For a simple lookup tool with two optional parameters and no output schema, the description covers the purpose, input methods, and return fields comprehensively. It is complete enough given the tool's complexity.

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% and the description largely repeats schema information. The description adds minimal value beyond the schema, such as 'up to 5 candidates' which is already in schema for the 'name' parameter. Thus, it does not significantly enhance 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 it performs Czech company checks from the ARES register, specifies two lookup methods (by ICO exact or by name, returning up to 5 candidates), and lists returned data fields. It distinguishes itself from sibling tools that target 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 explicitly indicates this tool is for Czech companies and provides lookup methods. While it does not explicitly state when not to use it, the sibling tool list implies alternatives for other jurisdictions. No direct exclusions are given, but the context is sufficient.

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 discloses the register source, return fields, and meaning of no_match. It also notes companies-only. This is good transparency for the tool's 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?

Four sentences, each informative. Front-loaded with purpose, followed by best practice, return details, and limitations. No 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 3 parameters, no output schema, and no annotations, the description covers the essential purpose, input recommendations, output structure, and scope (companies only). Could add more on output schema but sufficient 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?

Schema coverage is 67%; description adds 'strongly recommended' and 'exact match' for kvk_number, which is helpful. City parameter is undocumented in both schema and description, missing a chance for full clarity.

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 checks against the Dutch Central Insolvency Register, lists the specific procedures detected, and distinguishes from siblings by focusing on Dutch companies and KVK number.

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 a usage tip (best with KVK number) and implies company-only usage. It does not explicitly compare to sibling tools but sets clear 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?

No annotations provided; the description lists return fields (name, form, business line, dates, address, status codes, situations like bankruptcy, link) and states 'Companies only.' It lacks details on side effects, authentication, or rate limits, but provides reasonable 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?

Three concise sentences, front-loaded with purpose, then lookup methods and results, ending with scope ('Companies only'). No unnecessary words.

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 all key return information. Parameters are fully explained. The sibling tools provide context for the domain (country-specific checks). Complexity is moderate and well-addressed.

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. The description adds value by giving a format example for business_id (e.g., 0112038-9) and clarifying mutual exclusivity ('provide this or name').

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 checks Finnish companies from the YTJ register, with specific lookup methods (Business ID or name). It distinguishes from sibling tools which target 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 explains when to use each parameter (exact Business ID or name search) and limits to 5 candidates. It does not explicitly mention when not to use this tool or alternatives, but the sibling 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.

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

With no annotations, the description fully discloses the data source (INSEE Sirene register), returns (official legal name, status, legal form, NAF codes, dates, address, SIRET, link), and limitations (companies only, no director/officer data). No contradictions.

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?

Single paragraph efficiently conveys all essential information: source, lookup methods, output fields, and limitation. No unnecessary words, well-structured for quick reading.

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?

Despite lacking an output schema, the description thoroughly covers return values and behavior. All relevant details for an agent to decide on usage are present, making it complete for its complexity.

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 meaning by explaining that 'name' returns up to 5 candidates and 'siren' is exact 9-digit with an example, providing context beyond the 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?

Clearly states the tool performs official French company checks from the INSEE Sirene register, with specific lookup methods (SIREN or name). Distinguishes from sibling tools like check_uk_company and check_dutch_insolvency by specifying French scope and data limitations.

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 describes when to use (French company checks) and what it does not provide (no director/officer data). While it doesn't directly contrast with siblings, the list of sibling tools makes the 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 exist, so the description carries the burden. It discloses return fields (judgment nature, date, court, publication date, BODACC link) and a limitation (searchable set since 2008, no historical clearance). It also states 'Companies only', adding a behavioral constraint. Missing details on authentication or rate limits, but acceptable.

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, directly stating the tool's function and key details without extraneous words. Core information is front-loaded and each sentence contributes 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 one parameter, no output schema, and no annotations, the description covers essential aspects: input, return values, limitation, and scope. It could mention if there are any pagination or error handling details, but it is sufficiently complete for the tool's simplicity.

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 the single parameter 'siren' with 100% description. The description adds context: 'exact match; resolve a name via check_fr_company first', which goes beyond the schema definition and aids correct usage.

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

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

The description clearly states the tool does French insolvency checks against BODACC, specifying the types of procedures and announcements. It distinguishes itself from siblings like check_dutch_insolvency and check_fr_company by focusing on French insolvency and mentioning SIREN lookup.

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 implicit usage guidance by specifying the input (SIREN) and noting a no_match means no historical clearance. It also advises to resolve a name via check_fr_company first, indicating a recommended workflow. However, it 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, the description fully discloses the tool's behavior: it returns official name, organisation form, industry code, employee count, registration date, address, bankruptcy/liquidation flags, and a register link. It mentions the data source as open data. No contradictions or hidden aspects are apparent.

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 (three sentences) with no unnecessary words. It front-loads the authoritative source and purpose, then efficiently lists returned data and constraints.

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 thoroughly enumerates return fields. It covers all necessary context for a lookup tool: source, parameters, output, and limitations (companies only). No annotations are needed for this read-only 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 important context: 'exact' for organisation_number, 'up to 5 candidates' for name, and implies at least one is required despite schema showing both optional. This clarifies expected 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 an official Norwegian company check from a specific register, lists what it returns, and differentiates from sibling tools (other country checks). It specifies exact lookup by organisation number or name search.

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 Norwegian companies through its title and content. It explains the two lookup methods and notes 'Companies only,' which guides against misuse. However, it does not explicitly state when not to use or mention 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?

No annotations are provided, so the description must convey behavior. It states it is a read-only lookup (no destructive actions implied) and lists return fields. However, it does not disclose potential error states, rate limits, or authentication 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?

Three sentences, front-loaded with purpose and key features. 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?

The description covers input purpose, use case, and return fields (VAT status, REGON/KRS, address, account count, request_id). No output schema exists, so this is adequate. Could mention output format or error handling, but not necessary for basic 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. The description adds useful context beyond the schema: nip is '10-digit Polish NIP', bank_account is 'Optional 26-digit account number (NRB) to verify against the tax register', clarifying optional use and format.

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 checks the Polish VAT whitelist, verifies VAT status by NIP, and uniquely checks bank account registration. It distinguishes from sibling tools (other country checks) by specifying 'Polish' and 'official Ministry of Finance register'.

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 says 'the standard Polish invoice-fraud check before paying a supplier', providing clear context for when to use. It does not explicitly state when not to use or mention alternatives, but the sibling list implies other country-specific 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 details the search parameters and return fields (issuer, member state, authority, DTIs, white paper URL). It does not mention side effects, rate limits, or authentication requirements, but as a read-only lookup on a public register, the lack of destructive behavior is implied.

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

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

Three sentences, front-loaded with a question, no filler. Every sentence adds value: purpose, scope, input options, output summary, and usage advice. Highly 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?

The tool has 3 parameters with full schema coverage and no output schema, so the description compensates by describing the return fields. It covers all essential aspects (what, when, inputs, outputs). Minor omission: does not mention that the register is 'interim' (stated only once) or any limitations like latency. Still, it 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 description coverage is 100%, so baseline is 3. The description adds context by explaining what DTI stands for ('Digital Token Identifier ISO 24165') and clarifies that issuer name, LEI, or DTI can be provided. However, it does not add substantial new information beyond the 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 starts with a clear question summarizing the tool's purpose, specifies the exact registers queried (ESMA interim MiCA registers) and the entities (EMT/ART issuers, white papers). It distinguishes from sibling tools like check_crypto_firm and screen_eu_sanctions by focusing on MiCA notification status.

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

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

It states 'Useful before trusting a stablecoin or token sold as EU-regulated,' providing a concrete context. However, it does not explicitly exclude situations or mention alternative tools for related queries (e.g., check_crypto_firm).

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 details output fields and confirms the source. However, it does not mention rate limits, authentication, or error behavior, 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?

Two sentences, front-loaded with purpose and source, followed by usage and output details. No redundancy, perfectly concise.

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

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

Given no output schema, the description covers all key return fields. It lacks mention of error cases or prerequisites but is sufficient for a simple lookup tool with clear input constraints.

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 descriptions. The description adds value by noting mutual exclusivity of parameters and the 5-candidate limit for name search, going 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 identifies the tool as an official UK company check from Companies House. It specifies exact lookup by company number or name search, and lists specific return fields, distinguishing it from sibling tools focused on 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 explains how to use (exact number or name search) but does not explicitly guide when to choose this tool over siblings. It implies UK context but lacks explicit alternatives or 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?

With no annotations provided, the description fully discloses behavioral traits: it combines availability monitoring, on-chain USDC payment history, and EU regulatory status. It explicitly states 'Factual only — no scores; not being monitored is not negative, presence is no endorsement', setting clear expectations about the tool's output and limitations.

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, fitting into one paragraph without redundancy. Every sentence contributes meaningful information. It could be slightly more structured (e.g., bullet points), but the current form is clear 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?

The description covers inputs and behavior well but does not describe the output format. No output schema is provided, leaving agents uncertain about the return structure. This gap reduces completeness for a tool that returns factual data from multiple sources.

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 description adds value by explaining how the parameters combine ('by pay_to address and/or service URL') and what data they access (availability, payment history, regulatory status), going beyond the schema's basic 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 the tool's purpose: 'Trust check before paying an unfamiliar x402 service'. It specifies the inputs (pay_to address and/or service URL) and distinguishes itself from sibling tools (company/sanctions checks) by focusing on x402 services.

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 when to use the tool ('before paying an unfamiliar x402 service'). It does not explicitly state when not to use it or suggest alternatives, but given the sibling tools are for different purposes, the usage context is 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?

The description explains the data sources and output but does not discuss potential failure modes, rate limits, or side effects. Since no annotations exist, the description carries full burden; a simple read operation is sufficiently transparent, but more detail on data freshness or error states would improve 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 concise and front-loaded, stating the core function first. Every sentence adds value, with no redundant or extraneous content.

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?

Despite lacking an output schema, the description fully covers the return values: deviation in basis points, both source values, and timestamps. It is complete for the tool's purpose, though explicit field names or structure would enhance it.

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 zero parameters, and the description confirms 'No input required.' This adds no extra meaning beyond the schema but is clear and appropriate. Baseline for zero parameters is 4.

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 monitors the EURC peg by comparing the official ECB euro rate to the on-chain EURC/USDC price, returning a deviation in basis points. It distinguishes itself from all sibling tools, which focus on corporate and regulatory checks.

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 indicates the tool is for agents holding or pricing EURC, providing clear context. It does not list explicit alternatives or when-not to use, but no sibling offers a similar function, so the guidance is adequate.

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. Notes free, polling nature, and repetition condition. Lacks details on timeout, error handling, or return structure, but adequate for simple polling 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?

Extremely concise single sentence, front-loaded with key info: free, poll, handle, repeat. No wasted words.

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 tool simplicity (1 param, no output schema), description is fairly complete: explains action, prerequisite, and polling logic. Missing return format and error handling, but adequate for basic 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?

Only one parameter 'handle' with schema description. Description reinforces it's the handle from buy_phone_number, adding context beyond the schema. Schema coverage 100%.

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 polls for SMS code using handle from buy_phone_number. Verb 'poll' and resource 'incoming SMS code' are specific. Distinguishes from sibling buy_phone_number which buys the number.

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 mentions polling after buying a number and repeating until status is 'received'. Implies prerequisite of buy_phone_number, but no explicit exclusions 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 provided, but description fully discloses return fields (legal name, status, jurisdiction, etc.) and notes it only covers legal entities. Behavior is read-only, which is implied by 'lookup' and no mention of side effects.

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 four sentences, front-loaded with purpose. Could be slightly more concise, but each sentence adds distinct information (source, lookup options, return details, entity scope, usage tip). 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 no output schema, the description adequately explains return values for a simple lookup tool. Mentions per-record output with a link. No mention of pagination or error states, but for a simple lookup this is acceptable.

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

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

Schema covers 100% of parameters, but description adds value by clarifying that 'lei' and 'name' are alternatives, providing an example for jurisdiction ('NL'), and explaining the LEI format (20-character ISO 17442) succinctly.

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 does LEI lookup from GLEIF register, with options for LEI code or name search and optional jurisdiction filter. Distinct from sibling tools like check_crypto_firm or verify_eu_vat.

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?

Describes when to use ('ideal cheap first check before deeper company screening'), implying a preliminary step. No explicit when-not or alternatives listed, but context is clear enough for an agent to decide.

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 performs a live probe and checks monitoring history, and optionally verifies pay_to. It states 'Facts only — no scores', implying no subjective scoring. However, it does not explicitly confirm the tool has no side effects or destructive actions, which would be helpful 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 (4 sentences) and front-loaded with the core purpose. Each sentence adds distinct information: purpose, live probe details, monitoring history, optional verification, and a note about output style. No superfluous content.

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 the kind of facts returned (status, latency, x402 validity, uptime, etc.), providing a good sense of what the agent will receive. It does not specify the exact response format, but the listed details are sufficient for typical use. The presence of sibling x402_service_report is not addressed, but the description is otherwise 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 baseline is 3. The description adds context by explaining that the resource URL is probed live and that the pay_to is verified against the 402. This reinforces the schema but does not add substantial new meaning beyond what the schema already conveys.

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: 'Preflight before paying an x402 service'. It specifies exactly what the tool does: a live probe of the resource URL and monitoring history, with optional pay_to verification. This distinguishes it from sibling tools like check_x402_counterparty and x402_service_report.

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 context: use before paying an x402 service. It does not explicitly state when not to use it or compare to alternatives like x402_service_report. The note 'Facts only — no scores' provides some guidance on output nature, but exclusions are missing.

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 effectively discloses key behaviors: returns factual items with sources (not risk scores), billing only when both registers consulted, and limitation to companies. This goes beyond basic description.

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

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

Three concise sentences front-loaded with purpose, followed by input requirements and output format. No wasted words, each 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 or annotations, the description covers inputs, process, output format, and billing. Missing error handling or edge cases, but still fairly 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 coverage is 67% with descriptions for two parameters. The description restates requirements but adds no new semantics for legal_name, which remains undocumented. It does not enhance understanding 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?

The description clearly states it performs a comprehensive Dutch supplier screening combining VAT validation, insolvency check, and name cross-check. It distinguishes itself from siblings like verify_eu_vat or check_dutch_insolvency by being an aggregated single-call solution.

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 it's a 'pre-transaction check' and lists components, but does not explicitly state when to use this tool over alternatives (e.g., when only VAT check is needed). It provides usage context 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?

No annotations provided, but the description discloses matching behavior (normalised across aliases), output fields, and limitations. Could mention idempotence or side effects, but overall transparent for a screening 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?

Single focused paragraph, front-loaded with the main action, but could be split into bullet points for scanability. 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?

Despite no output schema, the description thoroughly explains return fields (source list, matched names, programmes, dates, links) and covers complexity of two lists and aliases.

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 a clear description for 'name'. Tool description adds value by explaining matching behavior and output structure, enhancing parameter meaning.

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 screens a company/organisation name against two specific sanctions lists (EU and UN) with normalized matching, distinguishing it from sibling tools that check company registries or other 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?

Explicitly tells when to use (for entity screening against EU/UN lists), what not to use it for (no persons, no other jurisdictions like OFAC), and clarifies that 'not_listed' does not mean clearance.

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 coverage differs per country, per-dimension reporting, types of insolvency signals, and that results are factual without scores. This provides sufficient behavioral context for a read-like 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 a single paragraph, front-loads the core purpose, and contains no redundant phrases. It is concise and informative, though could be slightly more structured 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 the complexity (4 params, no output schema, many siblings), the description adequately explains the tool's coverage and behavior. It could be improved by briefly noting the response structure, but the mention that each dimension reports coverage is helpful.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description adds overall context (e.g., routing by country, VAT validation types) but does not significantly enhance parameter understanding beyond what the schema already provides. Baseline score of 3 is appropriate.

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

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

The description explicitly states it performs 'European supplier screening' and details the specific checks (register profile, insolvency, VAT, sanctions, name verification) and per-country routing. This clearly distinguishes it from sibling tools that focus on single countries or specific checks.

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 this is a one-call solution for comprehensive EU supplier screening, but it does not explicitly state when to use this composite tool versus individual sibling checks (e.g., when screening multiple countries or dimensions). The context is clear but lacks explicit exclusions 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?

With no annotations, description carries full burden and excels: it discloses two modes with caching behavior, need for requester ID in consultation_proof, timestamp labeling, return of consultation number, meaning of 'not confirmed', machine-readable errors, and per-member-state availability. No contradictions or omissions.

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 a single, well-structured paragraph that front-loads the main purpose and then efficiently covers modes, behavior, and caveats. 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?

Despite no output schema, description provides enough detail about return values (timestamp, consultation number, machine-readable errors, member-state status). For a validation tool, this is sufficient for an agent to understand what to expect.

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 67%; description adds meaning to mode (fast may cache, consultation_proof always live and returns official number) and clarifies vat_number format ('Country code + national VAT number'). It enhances understanding beyond schema enum and 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 the tool validates EU VAT numbers against the VIES service, specifies two modes, and clarifies interpretation of results like 'not confirmed' not implying invalid company. Purpose is specific and distinguishes from siblings which are unrelated.

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 explains when to use fast vs consultation_proof mode, including that fast may use cache and consultation_proof requires requester identification. It also gives context on interpreting negative results. While no explicit when-not or alternatives, siblings are in different domains, so guidance is adequate.

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 behavior: data sources, metrics included, error response, and scope. It covers what the tool does and does not do (e.g., 'Facts only — no scores').

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 given the amount of detail, with no redundant sentences. It front-loads the purpose and then lists components efficiently.

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?

Despite lacking an output schema, the description fully explains what is returned (daily probes, uptime, events, payment details) and the error case. This is sufficient for agent invocation.

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

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

The single parameter 'host' has a schema description that lists examples and mentions '30-day monitoring history'. The tool description adds context about how the host is used, but the schema already covers coverage 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?

The description clearly states it provides a 'full reliability report on one x402 service', specifying the type and scope. It distinguishes from sibling tools like 'check_x402_counterparty' and 'preflight_x402_service' by focusing on historical monitoring 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 implicitly guides usage by describing the report content and error case (400 for unknown hosts). However, it does not explicitly compare with siblings or state when not to use this tool.

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