mingxin-mcp-server

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

Despite no annotations, the description discloses that results are mid-scenario estimates and not commitments, and that the model is a faithful port. This provides behavioral context beyond the schema, though it omits whether the tool is read-only or has side effects (implied harmless).

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?

Two concise sentences, front-loaded with the tool's purpose, followed by a caveat. No unnecessary words or repetition.

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?

With no output schema, the description does not specify what the tool returns (e.g., ROI value, format). It mentions mid-scenario estimates but lacks detail on output structure. Adequate but incomplete for a tool with no output schema.

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% with each parameter described, so the description adds no additional parameter meaning. Baseline 3 is appropriate as the schema already handles parameter semantics.

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 estimates ROI for adding a specific storage tier to a GPU inference cluster, using a specific model. It distinguishes itself from siblings (query_benchmark, search_mingxin_docs) by its unique estimation purpose.

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 usage for ROI estimation but does not explicitly tell the agent when to use this tool versus alternatives like query_benchmark or search_mingxin_docs. No when-not or alternative guidance is provided.

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?

Despite no annotations, the description adds behavioral context by stating numbers come from signed test reports and are reproducible via a GitHub link. It discloses the data source and confirms the results are hosted as PDF URLs, making the behavior transparent for a read-only query tool.

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 a single sentence that packs significant detail (specific metrics, model, hardware, reproducibility). It is efficient but slightly dense, yet every part adds value. Could be split for readability, but overall 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?

Given no parameters, no output schema, and the tool's simplicity, the description adequately covers what is returned (list of reports with PDF URLs) and the data's provenance. It is complete enough for an agent to understand the tool's output and 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?

The tool has zero parameters and 100% schema coverage (trivially), so the description does not need to add parameter meaning. Baseline of 4 is appropriate; no further explanation is required.

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 queries Mingxin's signed benchmark results for FX-series storage acceleration, listing specific performance metrics and output contents. It uses the verb 'Query' and specifies the resource (signed benchmark results), making the purpose highly clear and distinct from 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 implies usage when these specific benchmark results are needed, but provides no explicit guidance on when to use this tool versus the sibling tools 'estimate_roi' and 'search_mingxin_docs'. No exclusions or alternatives are mentioned, leaving the agent to infer usage context.

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?

With no annotations provided, the description carries the full burden. It discloses that the tool returns titles, URLs, and excerpts and supports Chinese and English. However, it omits details about behavior such as pagination, rate limits, or data freshness, which would enhance 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 two sentences with no wasted words. It front-loads the core purpose and returns, then adds language support. Every sentence serves a clear 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?

Given no output schema, the description correctly mentions return fields (titles, URLs, excerpts). All three parameters are accounted for in the schema with descriptions. The tool is a straightforward search, and the description covers key aspects for an agent to use it correctly.

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 schema already documents parameters. The description adds minimal value by restating language support and that query accepts Chinese/English, which aligns with the schema. It does not introduce new 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 the tool searches Mingxin's published articles on a specific topic (AI inference storage) and specifies the return fields (titles, URLs, excerpts). It distinguishes itself from siblings like estimate_roi and query_benchmark, which serve different purposes.

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 indicates when to use the tool (to search Mingxin's articles) but does not provide explicit guidance on when not to use it or mention alternatives. The context from sibling tools implies differentiation, but no direct comparison is given.

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