market_data_search_nameQuery stocks by code or name using local market data. Get fuzzy matches directly from the cache, avoiding external API lookups.
在本地 stock_info 中按代码/名称模糊搜索（替代每次现拉 gtimg 名称）。
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | market_data.db 路径 | /app/data/market_data.db |
| limit | No | 返回条数 | |
| keyword | Yes | 代码或名称关键词 |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It usefully discloses that the search is local and fuzzy rather than a live remote fetch, which is meaningful context. However, it does not mention staleness of local data, matching semantics such as case sensitivity, or that this is a read-only operation beyond the verb 'search'.
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 entire description is one focused sentence that front-loads the core behavior and includes the key usage rationale. There is no redundant or promotional content.
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 simple three-parameter schema and the presence of an output schema, the description is reasonably complete. It conveys the data source, search semantics, and the main reason to prefer this tool over live fetching. It could mention data freshness, but the output schema and defaults cover most operational needs.
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%, so the schema already documents all three parameters. The description adds only the 'code/name' mapping for keyword, which slightly reinforces meaning but does not go beyond what the keyword parameter description already 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 states a specific action (fuzzy search) on a specific resource (local stock_info) by code or name, and distinguishes itself from pulling gtimg names each time. This makes the tool's purpose immediately clear and separates it from potential data-fetching siblings.
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 explicitly frames the tool as a local substitute for repeatedly pulling gtimg names, giving clear context for when it should be used. It does not explicitly list exclusions or compare against sibling tools, but the substitution rationale is strong enough guidance for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.