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 adds 'up to 640 bars' and 'day/week/month periods', but these restate the input schema rather than revealing new behavior such as return format, error behavior, or whether the data is read-only. This is thin coverage for a tool with no annotation 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?

One clean sentence with no filler, and the core action is front-loaded. Some words ('day/week/month periods', 'up to 640 bars') duplicate schema details rather than adding new information, so it is concise but not maximally informative.

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 input side is fully covered by the schema (code, days, period), so an agent can construct a valid call. However, there is no output schema and the description never describes the shape of a K-line response, leaving the agent to guess how to consume the result. For a simple read tool this is a moderate gap, not a fatal one.

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%, and the schema documents defaults, ranges, and enum values for all three parameters. The description's mention of periods and bar limit adds no parameter meaning beyond the schema, so the baseline of 3 applies.

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 opens with 'Get historical K-line (candlestick) data for a stock', naming a specific verb, resource, and scope. The 'historical' qualifier and the K-line/candlestick terminology distinguish it from the sibling tools get_quote and search_stock even without naming them.

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 word 'historical' implies this tool is for past bar data rather than current quotes, but the description never tells an agent when to prefer it over get_quote or search_stock, nor does it mention any exclusions or prerequisites. Usage is only implied, not stated.

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 for behavioral disclosure. It only states what it does, not whether it is read-only, has rate limits, or any side effects. It does not mention authentication, error handling, or response behavior. 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 two sentences, front-loads the core purpose, and packs all necessary usage details (accepted formats, comma separation) without redundancy. Every sentence contributes meaning, making it highly efficient.

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 with one parameter and no output schema, but the description does not describe the return format or expected data structure. For a quote tool, the agent may need to know whether it returns a price object, a string, or errors. Given the lack of output schema and annotations, a bit more detail on what the caller should expect would improve completeness, but the core calling instructions are adequate.

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%, but the description adds valuable context beyond the schema: it explicitly mentions the 'sh' prefix format and clarifies that HK codes like '00700' and US codes like 'AAPL' are accepted. This enhances the agent's understanding beyond the schema's single example, justifying a score above baseline 3.

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 gets real-time stock quotes for three markets, lists accepted code formats, and implies a distinction from siblings (search_stock, get_kline) by focusing on real-time quote retrieval. The verb 'Get' plus the resource 'real-time stock quote' is specific and unambiguous.

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?

While no explicit alternatives or when-not-to-use are given, the purpose is clear enough that an agent would infer this tool is for quotes, not searching or historical data. The context of real-time quotes differentiates it from siblings without needing explicit exclusion statements.

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. It discloses that matches are returned with codes, but does not mention edge cases (e.g., no matches), result ordering, or how the limit parameter affects results. The behavior is mostly captured but lacks important contextual details.

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-loads the purpose, and contains no filler or redundancy. Every phrase adds useful information about supported inputs and outputs.

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 (2 params, no output schema, no annotations). The description explains the core purpose and input flexibility, but does not describe the result shape beyond 'stocks with codes' and omits notes about default limits or behavior when multiple matches exist. While adequate for a simple search, it leaves some operational details unspecified.

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 baseline is 3. The description adds the concept of pinyin abbreviation and partial code support, which slightly extends the schema's examples, but the schema already includes illustrative examples. The description does not add meaningful information about the limit parameter, so added value is minimal.

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 'search' and the resource 'stock', and specifies supported input formats (Chinese name, pinyin abbreviation, partial code). It also states the output ('matched stocks with codes'), which distinguishes it from siblings like get_quote and get_kline that retrieve specific data rather than 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 implies when to use the tool (when you have a partial identifier or name) through examples, but it does not explicitly state when to use this tool versus alternatives like get_quote or get_kline. No exclusions or alternative routing are mentioned, so guidance is merely implied rather than explicit.

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