leadoracle

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

With no annotations, the description must convey behavioral traits. It implies a read-only operation through 'look up' and lists the returned data types, which is helpful. However, it does not disclose possible nuances like multiple results, error behavior, or the fact that the company parameter may be optional, leaving some behavioral ambiguity.

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

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

The description is a single, front-loaded sentence that conveys the essential purpose and output fields without unnecessary words. Every element earns its place, making it highly concise and well-structured.

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 lookup tool with fully documented schema, the description adequately covers the purpose and expected return fields. The main gap is that it does not clarify the optionality of the company parameter or the return format, but the task is simple enough that the description is mostly 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 input schema already provides detailed descriptions and examples for both parameters (company and country), achieving 100% coverage. The tool description adds no extra parameter-specific semantics, 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 states the tool looks up company details and enumerates the specific fields returned (registration, status, address, incorporation date). The verb 'look up' and resource 'company details' distinguish it from sibling tools that handle domains, emails, people, or startups.

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 when needing company details, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. The sibling tool names suggest related lookup tools, but the description offers no direct comparison or alternative guidance.

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

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

With no annotations provided, the description carries the burden of disclosing behavior. It does list what data is returned (DNS, WHOIS, etc.), but it does not mention any side effects, authorization requirements, rate limits, or limitations. The core function is clear, but behavioral depth is minimal.

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 that front-loads the key concept ('Domain intelligence') and then enumerates the main data types. It is efficient and free of wasted words, though slightly terse in its omission of context.

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

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

For a simple one-parameter tool with no output schema, the description provides a useful summary of the return content. However, it lacks information about the response format, whether all listed fields are always included, and any error conditions. It is adequate but has clear gaps in completeness.

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

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

The input schema has 100% coverage for the single parameter 'domain', including an example. The description itself adds no further parameter-specific meaning, so it does not exceed what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly identifies the tool as providing domain intelligence, listing specific data categories (DNS, WHOIS, email patterns, LinkedIn/XING profile). This distinguishes it from sibling tools focused on companies, people, or health checks, though it lacks an explicit verb like 'retrieve' or '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 gives no guidance on when to use this tool versus alternatives. For example, it mentions 'email patterns' which overlaps with the sibling 'email_finder', but the description does not clarify how they differ or when one should be chosen over the other.

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 responsibility for behavioral disclosure. It doesn't state whether the tool uses public data, verifies addresses, returns single or multiple patterns, or requires at least one name. The phrase 'generate likely email patterns' hints at non-verification but doesn't explicitly disclose 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 a single concise sentence that immediately communicates the tool's function. It is appropriately sized for the tool's simplicity and contains no filler.

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

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

The tool is simple (3 optional params, no output schema), and the description is minimally adequate. However, it lacks context about how optional parameters interact, likely output format, and differentiation from sibling tools. Given the simplicity, a score of 3 seems appropriate.

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

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

Schema description coverage is 100%, with each parameter having a clear description. The tool description adds no extra meaning beyond what the schema provides, but the baseline is 3 when parameters are fully described.

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 ('Generate') and identifies the resource ('likely email patterns') and target ('person at a domain'). It clearly distinguishes from siblings like 'company_lookup' and 'domain_info' by focusing on email pattern generation. However, it doesn't explicitly contrast with 'person_search', which could also involve finding emails.

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 given on when to use this tool versus alternatives. It doesn't mention scenarios where this tool is preferred over 'person_search' or 'domain_info'. The intended use is only implied by the name and description.

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 responsibility for disclosing behavior. It only states 'LeadOracle server status' without explaining what the status output looks like, whether the call is read-only, potential errors, or latency implications. This is insufficient for an agent to know what to expect.

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 brief—four words—with no unnecessary content. It is front-loaded and to the point. However, it is a fragment rather than a complete sentence, which slightly reduces clarity, but for a zero-parameter tool, this is acceptable.

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, the description is minimal but not enough. There is no output schema and no explanation of what 'status' includes (e.g., uptime, version, latency). An agent would not know what the return value means or how to interpret 'server status'. The description should at least mention the type or format of the result.

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 tool has zero parameters, and the schema is empty. Per the guidelines, a baseline of 4 is appropriate since there are no parameter semantics to explain, and the description does not need to add anything 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 phrase 'LeadOracle server status' clearly indicates the tool checks the status of the LeadOracle server. It is distinguishable from sibling tools (company_lookup, email_finder, etc.) which handle data lookups. However, it lacks an explicit verb like 'check' or 'retrieve', making it slightly less clear.

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 no guidance on when to use this tool versus alternatives, or any context such as 'use to verify server health before other operations'. The intended usage is implied by the name 'health_check' and the short description, but no explicit direction is given.

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

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states the high-level action ('Find companies') without revealing any operational details such as whether results are limited by country, whether it returns a list, or if any authentication is needed. This leaves significant behavioral ambiguity 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 a single, well-structured sentence that immediately states the key action and target. It is maximally concise with no filler words, earning a high score for efficiency.

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

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

With no output schema and no annotations, the description is expected to provide more context about what the tool returns or how it behaves. It only states the purpose, omitting any mention of the response format, limitations, or additional behavioral nuances, making it incomplete for an agent to fully understand the tool's usage.

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

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

The input schema already provides 100% coverage of all three parameters with descriptions (limit, country, industry), so the baseline is 3. The description adds no extra meaning about parameters beyond echoing the 'industry' concept, but it doesn't need to since the schema is complete.

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

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

The description clearly states the tool's function with a specific verb ('Find') and resource ('companies') scoped by 'industry or sector'. This distinguishes it from sibling tools like 'company_lookup' which likely provides broader company search functionality.

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

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

The description implies the tool is for finding companies by industry or sector, but it does not explicitly contrast with alternatives like 'startup_search' or 'company_lookup', nor does it state when not to use this tool. Guidance is implied via the phrase 'specific industry', but no exclusions or alternative references are given.

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

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

With no annotations provided, the description carries the full burden. It mentions information types but does not disclose behavioral traits like whether it makes network calls, rate limits, or how results are aggregated. The term 'search' implies read-only but is not explicit.

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 sentence of 11 words, front-loaded with the action and resource. Every word contributes meaning, with no waste.

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

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

Given no output schema and no annotations, the description is the only source for return behavior. It names three types of info but does not describe result format, whether results are combined or separate, or error handling. Adequate for a simple search tool but with clear gaps.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema's parameter descriptions, and does not elaborate on how 'company' improves accuracy.

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 a specific verb ('Search') and resource (person's professional profile, Wikipedia info, news mentions). It distinguishes itself from sibling tools like company_lookup and domain_info, which focus on different 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?

No explicit guidance on when to use this tool vs alternatives. The description only states what it does, not when to prefer it over sibling tools, and no exclusions or alternatives are mentioned.

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 of behavior disclosure. The text only states the action and filter (sector) but does not explain what 'recently' means, how results are returned, whether pagination applies, or any data source limitations. This is minimal transparency for a search 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 a single sentence with no redundant words or phrasing. It efficiently conveys the core purpose without filler, making it easy to parse and remember.

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

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

For a tool with no output schema and no annotations, the description should provide more context about what the search returns, the meaning of 'recently', and how it differs from sibling tools. The current description is too sparse to fully guide an agent in a complex context.

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 both parameters with clear descriptions (query and country), achieving 100% coverage. The description does not add extra meaning beyond the schema, but it also does not contradict it. Baseline of 3 is appropriate given the schema's adequacy.

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 ('Find') and a clearly stated resource ('recently founded startups') with a context qualifier ('in a sector'). This distinguishes it from generic company lookups, though it does not explicitly differentiate from the sibling 'industry_companies' or mention when to prefer this tool.

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 gives no guidance on when to use this tool versus alternatives. It does not state prerequisites, exclusions, or mention sibling tools, leaving the agent to infer usage context from the name and schema alone.

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