Search Mingxin's published articles on AI inference storage (KV cache tiering, NVMe-oF all-flash arrays, LLM serving). Returns titles, URLs and excerpts. Supports Chinese and English.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language filter, default en | |
| limit | No | Max results (1-10), default 5 | |
| query | Yes | Search keywords (Chinese or English) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions returns (titles, URLs, excerpts) and language support, but omits important details like pagination, authentication, rate limits, or whether it is a read-only operation.
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?
Three sentences, front-loaded with verb+resource, no fluff. Every sentence adds information: what it searches, what it returns, and language support. Highly concise 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?
For a search tool with 3 parameters and no output schema, the description adequately covers purpose, return information, and language support. It could mention read-only nature or result ordering, but is largely 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 100%, providing baseline of 3. The description adds domain-specific context for 'query' (AI inference storage examples) and notes language support, beyond enum descriptions. This adds meaningful value.
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 searches Mingxin's published articles on AI inference storage, listing specific topics. It differentiates from siblings (estimate_roi, query_benchmark) by focusing on content search rather than ROI estimation or benchmarks.
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 searching specific articles but does not provide explicit when-to-use or alternatives. It lacks guidance on when not to use or how it compares to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct function: ROI estimation, benchmark querying, and documentation search. There is no overlap or ambiguity between them.
All tools follow a consistent verb_noun pattern in lowercase snake_case (estimate_roi, query_benchmark, search_mingxin_docs), making the naming predictable and coherent.
With 3 tools, the server is well-scoped for its purpose of providing information about Mingxin's storage solutions. Each tool serves a clear need without unnecessary clutter.
The three tools cover the essential areas: ROI estimation, benchmark evidence, and documentation search. Minor gaps like a contact or pricing tool exist, but the surface is largely complete for an informational server.