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

No annotations provided, so the description carries the burden. It indicates it's a query tool returning results, but does not confirm read-only behavior or disclose any side effects. The example suggests a safe operation, but more detail would improve 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 structured with sections (Args, Returns, Example) and is concise. It front-loads the purpose and provides necessary details without extraneous text. 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?

Given the 0% schema coverage and no annotations, the description covers parameters adequately with an example. However, it does not detail the output schema or the allowed query types, leaving some ambiguity. Has output schema but not described; could be more 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 coverage is 0%, so the description is the sole source for parameter meaning. It explains each parameter: queries (list of objects with query and type), strategy (parallel/sequential), and delay (seconds). The example clarifies usage, adding significant value beyond the schema's type-only information.

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 a batch query tool with complex strategies. It lists parameters and provides an example, differentiating from siblings which focus on specific analyses like gene evolution or pathway enrichment.

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 mentions strategy (parallel/sequential) and delay, implying when to use each, but does not explicitly state when not to use or compare to alternatives. Sibling tools are distinct in purpose, so guidelines are somewhat implied but not explicit.

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 disclose behavioral traits. It only describes parameters and returns, but does not mention side effects, read-only nature, permissions, or other important 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?

The description is well-structured with Args, Returns, and an Example section. It is concise and front-loaded with the tool's 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?

Despite having an output schema, the description provides only vague return information ('进化分析结果'). It lacks guidance on when to use among siblings and does not cover error cases or prerequisites.

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?

Input schema has 0% description coverage, but the description provides Chinese explanations for all 4 parameters with examples, adding meaningful semantic context beyond the schema types.

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 a gene evolution analysis tool and lists parameters, but does not differentiate from sibling tools like build_phylogenetic_profile or kegg_pathway_enrichment.

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 on when to use this tool versus alternatives. The description lacks any when-to-use or when-not-to-use advice.

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 disclose behavioral traits. It describes inputs and outputs but does not mention side effects, destructive actions, rate limits, or any limitations (e.g., number of genes allowed). The tool is implied to be read-only, but this is not explicitly stated.

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 well-structured with a title, arguments, returns, and an example. It is front-loaded with the purpose and efficiently conveys the essential information. However, the mix of Chinese and English may slightly reduce clarity for English-only agents, but the content is 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 the tool has an output schema, the description adequately summarizes return value as '系统发育图谱数据'. It provides parameter hints and an example, but lacks details on input constraints (e.g., valid species names) or error scenarios. It covers the basics but leaves some contextual 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?

With 0% schema description coverage, the description compensates by explaining each parameter in the docstring. 'gene_symbols' is described as a list of gene symbols, 'species_set' includes a default set of common model organisms, and 'include_domain_info' is a boolean for domain information. The example provides concrete usage context.

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 as '系统发育图谱构建工具' (phylogenetic profile construction tool) and provides a detailed docstring with arguments, returns, and an example. It differentiates from sibling tools like 'analyze_gene_evolution' by focusing specifically on building phylogenetic profiles from gene symbols and species sets.

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 lacks any guidance on when to use this tool versus the sibling tools. No conditions, prerequisites, or exclusions are mentioned. The example only shows a typical use case but does not explain when to choose this over 'analyze_gene_evolution' or 'kegg_pathway_enrichment'.

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 fully explains the tool's behavior, including auto-detection of query types and parameter effects. It lacks information on authentication or rate limits, but for a read-only tool, the disclosure is adequate.

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 well-structured with clear sections and examples, though it is somewhat lengthy. The key purpose is front-loaded, and each example adds value, but some redundancy exists (e.g., repeated query_type explanations).

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 (6 parameters, output schema exists), the description covers usage patterns comprehensively. It lacks details on error handling or performance, but for typical use cases it is sufficiently 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?

With 0% schema description coverage, the description compensates fully by listing all parameters, their defaults, and allowed values. It also provides context-specific examples (e.g., 'query' can be a string or array), adding substantial meaning beyond the raw 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 this as a unified data retrieval tool that handles various query types (gene, protein, region, etc.). It provides extensive examples and distinguishes itself from siblings by being a general-purpose query interface.

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 numerous examples showing when to use different query types and parameters, effectively guiding the agent. However, it does not explicitly contrast with sibling tools like advanced_query or smart_search, leaving some ambiguity about when to choose 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 full burden. It explains the purpose, inputs, and outputs, but does not disclose potential side effects (e.g., external API calls, computational cost, rate limits, or behavior under edge cases like empty gene lists). The behavioral transparency is adequate but not deep.

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 well-structured with sections for Args, Returns, and Examples, making it easy to scan. However, it is longer than necessary due to mixed Chinese/English, and the first line repeats the tool's purpose. It could be more concise without losing 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?

The description covers all parameters and the structure of the return value, which is sufficient given that an output schema exists (mentioned in context). It lacks some details like valid organism codes and edge-case handling, but overall it provides a complete picture for an enrichment analysis 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 description coverage is 0%, but the description adds meaning by explaining each parameter's role and default values. It clarifies that gene_list expects strings like 'TP53', organism defaults to 'hsa', and pvalue_threshold/min_gene_count have defaults. However, it does not provide valid ranges or examples for all parameters (e.g., organism codes), leaving some ambiguity.

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 performs KEGG pathway enrichment analysis on a gene list, specifying the resource (KEGG pathways) and the action (enrichment analysis). The title 'MVP版本' and examples distinguish it from sibling tools like advanced_query or smart_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 provides usage examples for different scenarios (cancer genes, mouse genes, stricter threshold), implying typical use cases. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusion criteria or guidance for when not to use it.

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 does not disclose whether the tool is read-only, whether it modifies data, or any special behaviors like rate limits or authentication. The examples hint at querying, but explicit behavioral disclosure is missing.

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 well-structured with sections for introduction, examples, args, returns, and additional examples. It is not overly verbose, though some redundancy exists (e.g., examples in both intro and dedicated section). The bilingual content is acceptable but could be streamlined.

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 output schema exists (though not shown), the description doesn't need to detail return values. However, it only vaguely states '智能搜索结果'. It lacks details on error handling, pagination, or ambiguous query behavior. For a moderately complex tool, more context would be beneficial.

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?

With 0% schema description coverage, the description compensates well by explaining each parameter: 'description' as natural language, 'context' with allowed values (genomics/proteomics/pathway), 'filters' as filter conditions with an example, and 'max_results' as maximum result count. This adds significant meaning 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 it performs intelligent semantic search by understanding natural language descriptions and executing queries. The examples illustrate the purpose well, but it does not explicitly differentiate from sibling tools like 'advanced_query' or 'get_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 implies use for natural language queries through examples, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'advanced_query' for structured queries). No when-not-to-use or exclusion criteria are provided.

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