usensedata-mcp-server-query-china-company

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

With no annotations provided, the description carries the full burden. It discloses the fuzzy search behavior and that it returns a list, but lacks details on pagination, result limits, error conditions, or authentication requirements. It adds some context but is incomplete for behavioral transparency.

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

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

The description is front-loaded with the core purpose and includes a clear usage guideline in two concise sentences, with no wasted words or redundant information.

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 single-parameter input, no output schema, and no annotations, the description adequately covers purpose and usage. However, it could improve by hinting at the output structure or error handling, though the low complexity makes this less critical.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds minimal value by mentioning 'company name keywords' but doesn't provide additional syntax, format, or examples beyond what the schema states.

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 with specific verbs ('fuzzy search', 'return a list') and resources ('company name keywords', 'enterprise information'), and it distinguishes from siblings by focusing on name-based search rather than specific data categories like litigation or trademarks.

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 explicitly states when to use this tool ('to retrieve the company's full name before calling other tools that require it'), providing clear guidance on its role as a preparatory step for sibling tools that need exact company names.

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 behavioral disclosure. It mentions the tool queries information but does not describe what 'abnormal business operation' entails, the data source, potential rate limits, error conditions, or the format of returned information. For a query tool with no annotation coverage, this leaves significant gaps in understanding its 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?

The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose and usage guidelines without unnecessary details. Every sentence adds clear value, making it efficient 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?

Given the tool's complexity (a query with one parameter) and the lack of annotations and output schema, the description is partially complete. It covers purpose and usage well but lacks details on behavioral aspects like what data is returned or error handling. It meets minimum viability but has clear gaps in transparency.

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 description coverage is 100%, with the parameter 'keyWord' documented as 'company full name.' The description adds minimal value beyond this by reiterating the need for the company's full name but does not provide additional context like formatting requirements or examples. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.

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 specific action ('Query') and resource ('abnormal business operation information of a company'), and distinguishes it from siblings by focusing on abnormal business operations rather than basic info, change records, or other company data. It explicitly mentions using the company's full name as input, which aligns with the parameter.

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

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

The description provides explicit guidance on when to use this tool: it instructs to 'use the fuzzy query tool to obtain the company full name before calling this tool,' naming the sibling tool 'fuzzy_query_company' as a prerequisite. This clearly defines the workflow and alternative tool usage.

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 describes the query/return behavior and mentions the need for a company full name obtained via another tool. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what happens with invalid inputs. The description adds some context but leaves significant gaps.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence states the purpose and scope, while the second provides crucial usage guidance. There's zero wasted text, and the most important information (what it does and when to use it) is front-loaded.

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

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

Given 1 parameter with 100% schema coverage but no annotations and no output schema, the description is moderately complete. It covers purpose and usage guidelines well, but lacks behavioral transparency details that would be important for a query tool (like response format, error handling, or limitations). The absence of output schema means the description should ideally describe return values, which it doesn't.

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% (the single parameter 'entname' has description 'company full name'), so the baseline is 3. The description adds marginal value by reinforcing that the parameter should be a 'company full name' and suggesting it should come from the fuzzy query tool, but doesn't provide additional semantic context 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 the tool's purpose: 'Query and return basic information such as company name, legal representative, and registered capital by company full name to understand the company overview.' This specifies the verb (query/return), resource (company basic info), and scope (overview). However, it doesn't explicitly differentiate from siblings like 'verify_company_name_and_president' which might have overlapping 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 provides explicit usage guidance: 'Please use the fuzzy query tool to obtain the company full name before calling this tool.' This clearly states a prerequisite and references the sibling tool 'fuzzy_query_company' as an alternative for obtaining the required parameter. It effectively tells when to use this tool (after obtaining full name via fuzzy query).

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 of behavioral disclosure. It states it's a query operation, implying read-only behavior, but doesn't mention potential side effects, authentication needs, rate limits, or what the output looks like (e.g., format, pagination). For a tool with zero annotation coverage, this is insufficient.

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, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes directly to understanding the tool's function.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'change records' entail, the return format, or any behavioral traits like error handling. For a query tool with no structured output documentation, more context is needed to guide the agent effectively.

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

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

The description adds minimal value beyond the input schema, which already has 100% coverage. It mentions 'full name' for the 'entName' parameter, but the schema description ('company full name') is equivalent. No additional semantics like name format or examples are provided, so it meets the baseline for high 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 action ('Query') and the target resource ('change records of a company'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'query_company_basic_info' or 'query_company_court_litigation_related_info' that also query company information but for different 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 provides no guidance on when to use this tool versus alternatives. It mentions querying by 'full name' but doesn't specify if this is the only method, when to choose it over 'fuzzy_query_company', or any prerequisites like company existence. This leaves the agent without clear usage 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool obtains information (implying read-only) and requires a company full name, but lacks details on permissions, rate limits, error handling, or response format. For a query tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is two sentences that are front-loaded with the core purpose, followed by a usage guideline. It avoids unnecessary details, though the second sentence could be slightly more concise (e.g., 'Use fuzzy_query_company first to get the exact name.').

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

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

Given the tool's complexity (simple query with one parameter), no annotations, and no output schema, the description is moderately complete. It covers purpose and usage but lacks behavioral details like response format or error cases. It's adequate for basic use but has clear gaps in transparency.

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

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

The input schema has 100% description coverage, with the 'name' parameter documented as 'company full name.' The description adds minimal value by restating this ('by its full name') and emphasizing the need for accuracy via the fuzzy query tool. This meets the baseline of 3 when schema coverage is high.

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: 'Obtain the litigation-related information of a target company in court by its full name.' It specifies the verb ('obtain'), resource ('litigation-related information'), and target ('target company'). However, it doesn't explicitly differentiate from sibling tools like 'query_company_abnormal_business_operation' or 'query_company_basic_info' beyond mentioning 'litigation-related' content.

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

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

The description provides explicit usage guidance: 'Please use the fuzzy query tool to obtain the company full name before calling this tool.' This names a specific alternative tool ('fuzzy_query_company') and states a prerequisite action, clearly indicating when to use this tool versus alternatives.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions that the tool queries investment information, it lacks details about behavioral traits such as whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or what the return format looks like. For a query tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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

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

The description is highly concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and usage guidelines. Every sentence earns its place: the first explains what the tool does, and the second provides critical prerequisite information. There is no wasted verbiage or 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 the tool's complexity (a single-parameter query with no output schema and no annotations), the description is partially complete. It adequately covers the purpose and usage guidelines but lacks details on behavioral transparency (e.g., read/write nature, response format) and parameter semantics beyond what the schema provides. For a query tool, this leaves the agent with insufficient context about what to expect from the tool's operation.

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 description coverage is 100%, with the single parameter 'entName' fully documented as 'company full name'. The description adds minimal value beyond this, only reiterating that the tool operates 'by the company's full name' and suggesting the fuzzy query tool to obtain it. No additional semantic context (e.g., format requirements, examples, or constraints) is provided, so the baseline score of 3 is appropriate given the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Query external investment information, such as the amount, shareholding ratio, shareholder type, etc., by the company's full name.' This specifies the verb ('query'), resource ('external investment information'), and scope ('by the company's full name'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this tool from its siblings beyond mentioning the fuzzy query tool as a prerequisite.

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

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

The description provides explicit usage guidance: 'Please use the fuzzy query tool to obtain the company full name before calling this tool.' This clearly states a prerequisite and references an alternative tool (fuzzy_query_company) for obtaining the required parameter. It effectively tells the agent when to use this tool (after obtaining the exact company name) and what to use instead for the preliminary step.

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 mentions the tool queries 'registration information' but doesn't disclose behavioral traits like whether it's read-only, what format the results are in, if there are rate limits, or authentication requirements. For a query tool with zero annotation coverage, this leaves significant gaps in understanding its 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?

The description is highly concise and well-structured with two sentences: the first states the purpose, and the second provides usage guidelines. Every sentence earns its place, and it's front-loaded with the core functionality.

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

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

Given the tool's complexity (simple query with one parameter), lack of annotations, and no output schema, the description is adequate but incomplete. It covers purpose and usage well but lacks details on behavior, output format, or error handling. For a query tool with no structured output documentation, more context would be 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 description coverage is 100%, with the parameter 'entName' documented as 'company full name'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Query the registration information of an company's software copyrights by its full name.' It specifies the verb ('query'), resource ('software copyrights'), and scope ('by its full name'). However, it doesn't explicitly differentiate from siblings like query_company_trademark_list, which queries different intellectual property 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 provides explicit usage guidance: 'Please use the fuzzy query tool to obtain the company full name before calling this tool.' This names the alternative tool (fuzzy_query_company) and specifies a prerequisite step, giving clear context for when to use this tool versus alternatives.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool retrieves trademark data but lacks details on permissions, rate limits, error handling, or response format. For a query tool with no annotations, this leaves significant gaps in understanding its operational 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?

The description is concise and well-structured in two sentences. The first sentence clearly states the purpose, and the second provides essential usage guidance without unnecessary details, making it efficient and front-loaded.

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

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

Given no annotations and no output schema, the description adequately covers the tool's purpose and usage prerequisites. However, it lacks details on behavioral aspects like response format or error conditions, which are important for a query tool. It meets minimum viability 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?

Schema description coverage is 100%, with the parameter 'entName' documented as 'company full name'. The description adds context by emphasizing the need for the 'full name' and linking it to the fuzzy query tool, but does not provide additional semantic details beyond what the schema already covers, aligning with the baseline score.

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: 'Obtain the list of an company's trademarks, including trademark names, company names, and statuses, by its full name.' It specifies the verb ('obtain'), resource ('trademarks'), and scope ('list'), but does not explicitly differentiate it from sibling tools like query_company_basic_info or query_company_software_copyright_info, which might also retrieve company-related 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 provides explicit usage guidance: 'Please use the fuzzy query tool to obtain the company full name before calling this tool.' It names the alternative tool (fuzzy_query_company) and specifies when to use it (to get the correct company name), offering clear prerequisites for effective use.

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

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

With no annotations provided, the description carries full burden. It discloses the verification logic and return values (0/1), but doesn't mention error handling, performance characteristics, or what constitutes 'consistency' (exact match vs fuzzy). It adds some behavioral context but leaves gaps.

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

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

Two concise sentences with zero waste: first states purpose and return values, second provides prerequisite guidance. Every sentence earns its place by adding essential information.

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 verification tool with 2 parameters, 100% schema coverage, and no output schema, the description is adequate but incomplete. It explains the verification purpose and workflow but doesn't define what 'consistency' means or how the verification is performed, leaving the agent to infer the mechanism.

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

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

Schema description coverage is 100%, so the schema already documents both parameters adequately. The description doesn't add meaning beyond what the schema provides about operName and entName, maintaining the baseline score.

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 consistency between a legal representative's name and company full name, with specific return values (0 for consistency, 1 for inconsistency). It distinguishes from siblings by focusing on verification rather than querying, though it doesn't explicitly name alternatives.

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

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

The description provides explicit guidance to use the fuzzy query tool first to obtain the company full name, establishing a clear prerequisite workflow. However, it doesn't specify when NOT to use this tool or name alternative verification methods.

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