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

With no annotations, the description fully discloses concurrency (ThreadPoolExecutor with 8 workers), caching (lru cache), single-failure tolerance, and fallback logic for bank metrics. Return structure is detailed with example JSON.

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?

Well-structured with clear sections for parameters, return fields, and implementation details. Every sentence adds value without redundancy. Length is appropriate for a complex tool.

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?

No output schema, yet description provides complete return structure with example JSON, concurrency, caching, and error handling. Covers all behavioral aspects needed for correct 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?

Input schema has 0% coverage, but description fully explains stock_codes formats, year, metrics default and optional, and derived ROE. Provides examples and constraints, compensating completely for schema gaps.

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 compares annual reports of N peer companies horizontally, computes ranks, min/max, mean, std, and derived ROE. It distinguishes from siblings like cross_check_balance and get_three_statements by specifying peer comparison logic.

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?

Explicitly recommends 2-10 companies, describes default metrics, and explains derived ROE always included. It doesn't explicitly state when not to use or alternatives, but provides clear context for appropriate 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?

No annotations given, but description fully covers behavior: checks three specific equations with tolerance, returns passed/failed/skipped status, handles missing fields gracefully, and notes annual-only support.

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?

Well-structured with intro, parameter details, return format example, and list of checks. Every sentence is informative and 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?

Despite no output schema, description provides full return example and explains all statuses and tolerance. Parameter semantics are fully covered, and sibling references add 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?

Adds significant meaning beyond schema: explains stock_code format and links to sibling tool, clarifies year only supports annual reports, and includes example values.

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?

Clearly states the tool performs financial cross-check validation among three statements, distinguishing it from siblings like get_three_statements and compare_peers.

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?

Provides parameter specifics (stock_code supports multiple formats, year only annual reports) and lists the three checks. Does not explicitly exclude use cases, but context suffices.

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 bears full weight. It discloses caching behavior, data source, currency, unit, and field naming conventions, but does not mention authentication or 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 well-structured with a brief intro, bullet points for parameters, and a clear return format, though it could be slightly more 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's simplicity and lack of output schema, the description covers all relevant aspects: purpose, parameters, return structure, data source, caching, and units, making it fully informative.

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 adds crucial detail: multiple accepted formats for stock_code and the requirement that year be an integer for annual reports only.

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 verb (拉取/fetch), resource (年报三大财务报表), and scope (A股某只股票某年), distinguishing it from sibling tools like compare_peers and cross_check_balance.

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 specifies that only annual reports are supported and provides parameter formats, but does not explicitly compare to sibling tools or state when not to use this tool.

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