Global business entity verification, sanctions screening (OFAC/UN/EU/UK HMT), UBO mapping, and jurisdiction risk scoring for AI agents. Pay per call in crypto (USDT/USDC/BTC/ETH). Built by ARM Consultancy LLC, UAE.
Map ultimate beneficial ownership (UBO) using GLEIF Relationship API, Companies House PSC Register (UK), and OpenCorporates corporate network.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Ownership chain depth (1-3) | |
| entity_name | Yes | Company name | |
| jurisdiction | Yes | ISO 2-letter country code (e.g. GB, DE, US) | |
| registration_number | No | Company registration number — improves UK PSC lookup accuracy |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description alone must disclose behavior. It lists data sources but does not explain what is returned, how failures and gaps are handled, how jurisdictions are treated, or what side effects (if any) exist. An agent cannot foresee whether missing data, network errors, or multi-source conflicts will occur.
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 one concise, front-loaded sentence with no redundant words. The data-source list carries useful scope information without requiring extra length.
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 multi-source UBO discovery operation with no output schema or annotations. The description does not explain what the returned ownership map looks like, how depth is applied, what coverage limitations are (e.g., UK PSC applicability), or what happens when data is missing. An agent cannot fully predict tool behavior without additional information.
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 covers all parameters with descriptions, so the baseline is 3. The description adds little parameter-specific meaning beyond implying that entity_name and jurisdiction are relevant to the UBO search; it does not explain optional parameters like depth or registration_number or their interaction with data sources.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Map') and resource ('ultimate beneficial ownership'), and specifies data sources (GLEIF, Companies House PSC Register, OpenCorporates). This clearly differentiates it from siblings that score risk, screen sanctions, or verify 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 implies a use case: mapping ownership chains and UBOs. However, it does not explicitly state when to choose this tool over the sibling tools, nor does it provide any exclusions or prerequisites. The usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a 0-100 jurisdiction risk score using FATF black/grey list status, Basel AML Index, and sector risk indicators.
| Name | Required | Description | Default |
|---|---|---|---|
| sector | No | Business sector (optional) | |
| entity_name | Yes | Entity name | |
| jurisdiction | Yes | ISO 2-letter country code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It does provide useful behavioral context by explaining the methodology and the numeric output range. However, it does not disclose whether the score is cached or live, whether it may call external services, what happens with an invalid or unsupported jurisdiction, or any rate-limiting concerns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence with no wasted words. It front-loads the most important information: what the tool generates, the output range expected, and the inputs used to calculate the score.
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 captures the output and method, but without an output schema or annotations, it leaves some operational gaps, such as what the returned payload looks like, whether plausible entity data is required, and how edge cases are handled. Still, the essentials for invoking the tool and interpreting the primary output are present.
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 provides descriptions for all parameters, so the baseline is 3. The description adds some useful semantics by linking 'sector' to sector risk indicators, but it does not clarify important edge cases such as jurisdiction format, entity contribution, or how to pass unsupported jurisdiction codes.
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 generating a jurisdiction risk score, states the numeric output range (0-100), and names the key data inputs (FATF list status, Basel AML Index, sector indicators). It is specific enough to stand apart from the sibling tools by intent, though it does not explicitly contrast itself with screen_sanctions or verify_entity.
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 usage context is implied by the description: this is the tool to call when a jurisdiction risk score is needed, unlike sanctions screening, ownership mapping, or entity verification. However, it does not explicitly state when to prefer this tool over sibling tools, nor does it describe 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.
Screen an entity against official OFAC, UN Security Council, EU Consolidated, and UK HMT sanctions lists. Data downloaded directly from government sources.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | Country of incorporation or operation | |
| entity_name | Yes | Entity name to screen | |
| entity_type | No | Type of entity |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does add useful context by stating that the data originates directly from government sources, but it does not indicate whether the tool returns a boolean, a list of matches, or a report, nor does it mention matching behavior or potential 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?
The description is two concise sentences with no wasted words. The core function and target lists are front-loaded, and the data-source note is placed as a meaningful secondary detail.
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 screening tool with three parameters, the description is near complete. However, with no output schema and no statement about what the response contains (e.g., matches or a concise screening decision), an agent still has to guess at the expected return behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and all parameter descriptions are adequate, which establishes a baseline of 3. The description adds no extra parameter-level details beyond the schema, so a 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific action ('Screen an entity') and names the exact resources (OFAC, UN Security Council, EU Consolidated, UK HMT sanctions lists). This clearly distinguishes it from sibling tools like map_ownership, score_risk, and verify_entity.
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 use case is implied: use this tool when you need to screen an entity against sanctions lists. However, there is no explicit 'use when' statement, no mention of when to use another tool, and no exclusions such as specifying verification or risk scoring are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a business entity — registration status, incorporation date, legal standing, and registered address. Data sourced from GLEIF, Companies House, SEC EDGAR, and OpenCorporates.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_name | Yes | Company name to verify | |
| jurisdiction | Yes | ISO 2-letter country code (e.g. GB, DE, AE, US) | |
| registration_number | No | Company registration number (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It does add useful context by naming the data sources and the kind of data returned. However, it does not mention potential discrepancies between sources, failure modes, coverage limitations, or what happens when an entity 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 a single efficiently written sentence with no filler. It communicates the target, the result, and the data provenance without repetition or unnecessary detail.
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 is sufficient for an agent to understand the tool's purpose, the kind of data it returns, and where that data comes from. The schema covers parameter meanings. No output schema exists, but the description enumerates the main output concepts. Minor missing details such as error behavior and source-specific limitations prevent a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides full descriptions for all three parameters, giving 100% schema description coverage. The description adds context about what the tool does but does not add parameter-specific meaning beyond what the schema already documents.
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 that the tool verifies a business entity and enumerates the specific attributes it returns: registration status, incorporation date, legal standing, and registered address. While it does not name sibling tools, the verb and resource make its purpose evident and distinguishable from map_ownership, score_risk, and screen_sanctions.
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 tool should be used when an agent needs registration-level verification of a business entity, but it does not explicitly say when to use it instead of alternatives or when not to use it. The sibling tool names suggest different purposes, but the guidance is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Add one secure layer between your agents and this server.
Each tool targets a distinct intelligence function: ownership mapping, jurisdiction risk scoring, sanctions screening, and entity verification. While some data sources overlap, the purpose and output of each tool are clearly separable.
All tool names follow a consistent verb_noun pattern: map_ownership, score_risk, screen_sanctions, verify_entity. The verb clearly indicates the action and the noun clearly indicates the subject, making the naming predictable and intuitive.
Four tools is a well-scoped set for an entity intelligence API. Each tool covers a meaningful and distinct part of the domain without redundancy or bloat.
The core entity intelligence workflow is covered: verify the entity, check ownership, assess risk, and screen for sanctions. Minor gaps exist, such as no explicit search/discovery tool or historical monitoring, but the primary lifecycle is complete.