due-diligence

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

With no annotations provided, the description takes on the full burden. It discloses the lawful-basis requirement and that the purpose is logged, which are behavioral traits. It also implicitly signals read-only access by stating it returns declared data, though it doesn't cover other operational details like 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 two sentences: the first states the function and output, the second states the key legal constraint. Every word earns its place 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?

For a simple two-parameter tool with no output schema, this description sufficiently covers the purpose, the returned data (ownership percentages, control basis), and the regulatory prerequisite. It does not leave obvious gaps for the agent's decision-making.

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

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

The schema already describes both parameters fully (code pattern and purpose enum with logging note), achieving 100% coverage. The description adds no new parameter detail beyond repeating that purpose is required and logged, so it remains at the baseline.

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's function: retrieving declared beneficial owners of a Lithuanian legal entity, including ownership percentages and control basis. This specific resource and content distinguishes it from sibling tools like get_court_cases or get_participants.

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 the explicit condition that data is served under the legitimate-interest regime and requires a purpose, which is logged. This provides clear context on when usage is appropriate, though it does not explicitly mention 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?

No annotations are provided, so the description carries the full burden. It discloses the core behavioral scope (returns hearings where the entity appears as a party), which is useful. However, it does not mention potential limitations, data source, or handling of edge cases like no results, leaving some behavioral details unspecified.

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?

A single, focused sentence that front-loads the essential purpose without any filler. Every word contributes meaning and the structure is immediately parsable.

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—one required parameter, no annotations, and no output schema—the description provides adequate context for an agent to understand what the tool does. However, it could be enhanced by noting any return format or caveats, though not strictly necessary for such a direct query 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?

The input schema already provides a full description of the single 'code' parameter, including its pattern and meaning. The tool description adds no additional semantic information about this parameter, so the schema carries the load and the 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 clearly states the tool returns court hearings where the entity is a party, making the resource and scope unambiguous. It distinguishes from sibling tools by focusing on court cases rather than ownership, real estate, seizures, etc. Though phrased as a noun phrase rather than a verb, the purpose is effectively conveyed.

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 intended use is implied by the description (i.e., retrieve court cases involving a Lithuanian entity), but no explicit guidance is given on when to use this tool versus alternatives. There are no exclusions or references to sibling tools, so the guidance is only implicit.

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 discloses the legal/processing requirement of a purpose due to beneficial-owner data, and notes the cost advantage. However, it omits details such as response format, authorization needs, or whether the data is real-time or cached, leaving gaps for a complex aggregation 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?

Three sentences, each earning its place: the first verb+resource summary, the second cost benefit, the third prerequisite. No wasted words, front-loaded with the core 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?

The description enumerates all data types included, the purpose requirement, and pricing, which is good for a composite tool with no output schema. It lacks a note on return structure or any limitation beyond purpose, but given the listing of contents, 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?

The schema already covers both parameters with descriptions (100% coverage), so the baseline is 3. The description adds the context that purpose is required because of beneficial-owner data, which the schema already states. No additional semantic value is provided for 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 the tool returns a comprehensive report ('whole picture') including profile, participants, beneficial owners, real property, seizure acts, and court cases. This specific verb-resource combination distinguishes it from sibling tools that focus on individual 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 when to use this tool: when you need the full picture, it is more economical than calling the individual part-tools ('cheaper than the parts bought separately'). It does not explicitly list when not to use it, but the comparison with alternatives provides 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?

Annotations are absent, so the description carries the full burden. It only adds the adjective 'current' to indicate a snapshot, but does not disclose whether the operation is read-only, any access requirements, rate limits, or response characteristics. This is insufficient behavioral 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?

The description is a single, concise sentence with no redundant content. It is well-structured and easy to parse immediately.

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 simple schema and lack of output schema, the description still feels incomplete. It does not explain what data will be returned (e.g., names, percentages), when to choose this over siblings, or any caveats. The description reads more like a label than a full tool explanation.

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 a complete description of the 'code' parameter, including a pattern and explanation. The description's mention of 'Lithuanian legal entity' essentially duplicates the schema. With 100% schema coverage, 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 states the resource (participants/shareholders/members) and scope (Lithuanian legal entity), and the tool name includes 'get' to indicate retrieval. It differentiates from sibling tools like get_beneficial_owners by specifying shareholders and members, though it is phrased as a noun phrase rather than an explicit action statement.

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 is provided on when to use this tool versus alternatives like get_beneficial_owners or lookup_company. The description does not mention any context, prerequisites, 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?

No annotations are provided, so the description carries the transparency burden. It discloses that the tool reads registered property data and includes rights, but does not mention any read-only guarantee, pagination, or potential error conditions. This is adequate but not rich.

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

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

The description is two sentences, front-loaded with the main purpose, and contains no filler. Every word adds value, including the 'Legal entities only' clarification.

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 single-parameter get tool with no output schema, the description covers what the tool returns, the scope (registered property, rights), and the entity-type restriction. It could mention whether results are paginated or list details, but this is not essential for basic 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 'code' is fully documented in the schema with a pattern and description, giving high schema coverage. The tool description adds no extra parameter meaning, but baseline 3 is appropriate since the schema already carries the semantic load.

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 returns real property registered to a Lithuanian legal entity, including the rights recorded per object. This uses a specific resource (real property of legal entities) and distinguishes itself from sibling tools like get_court_cases or get_participants, which cover 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 explicitly restricts usage to legal entities, signaling when not to use it (individuals). It does not name alternative tools for other property types or scenarios, but the context is clear enough that an agent can infer this is the go-to tool for real estate holdings.

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 transparency burden. It adds a valuable behavioral trait: an entity with no acts returns a clean record, not a missing answer, preventing misinterpretation. However, it does not disclose other behaviors such as read-only status, potential pagination, or rate limits, which are expected for a data-retrieval 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 two sentences, front-loaded with the core object, and includes a useful clarification about empty results. Every word earns its place, with 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?

For a simple single-parameter read tool with no output schema, the description provides adequate context: what data is returned and how to interpret empty results. It lacks details on response structure or pagination, but the listed components (institution, parties, seized assets) give sufficient expectation.

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% for the single parameter 'code', which is described as a Lithuanian legal-entity code with a pattern. The tool description adds no additional parameter semantics beyond what the schema already provides, so the 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 clearly identifies the resource as property seizure acts for a Lithuanian legal entity, specifying the institution, parties, and seized assets. It distinguishes itself from sibling tools (court cases, real estate, participants, etc.) by focusing on seizures, though it lacks an explicit verb like 'retrieve' or 'list.'

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 offers no explicit guidance on when to use this tool versus alternatives. It does not mention sibling tools or exclusions. The only usage hint is the clean-record note, which clarifies interpretation of empty results but not selection 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?

With no annotations, the description carries the burden of disclosing behavioral traits. It states 'Real-time from the state register,' which implies read-only data retrieval and freshness, but it does not explicitly confirm non-mutating behavior nor discuss edge cases like invalid codes or data availability for dissolved entities.

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, compact sentence that front-loads the core purpose and lists return contents without filler. It is appropriately sized and every word earns its place.

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 one-parameter lookup with no output schema, the description lists the key return fields and the data source, giving agents ample information to judge the response. However, it lacks explicit error behavior and leaves 'current management' slightly ambiguous, so minor gaps remain.

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 fully documents the single 'code' parameter with a pattern and a description, so schema coverage is 100%. The tool description adds no additional semantic detail beyond the schema, making the baseline score of 3 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 states the tool returns a 'Core registry profile of a Lithuanian legal entity' and enumerates the specific fields (name, legal form, status, address, dates, capital, activities, management), which distinguishes it from sibling tools that focus on narrower aspects like beneficial owners or court cases.

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 establishes clear context by positioning this as the core company profile tool, implying it's the first stop for general entity information. However, it does not explicitly name alternative tools for specialized data or state when not to use this tool, so it lacks explicit exclusions.

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