find_best_match

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 uses character similarity and only considers the first page of search results, which is a key behavioral constraint. However, it does not mention potential edge cases like no match found or whether it performs network requests.

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 concise sentences, front-loaded with the main purpose and followed by the matching algorithm detail. Every word earns its place, with no redundant information.

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 has an output schema, the description need not explain return values. It covers the core scope (matching on first page, using similarity) and parameters sufficiently. It omits discussion of edge cases, but the simplicity of the tool and the output schema compensate.

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 both parameters are already documented. The description adds minimal extra meaning beyond reinforcing that the title is used for matching and the default search type is '篇名'. It does not further elaborate parameter usage.

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 finds the most matching paper in CNKI based on the input title, using a character similarity algorithm on first-page search results. This specific verb+resource combination distinguishes it from siblings like search_cnki and get_paper_detail.

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 the tool is for when you have a paper title and want the best match from CNKI, but it does not explicitly mention when to use it versus alternative tools. It lacks explicit alternatives or exclusions, so guidance is only implicit.

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