Analyze Binaries

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

With no annotations, the description carries the full burden and does well: it discloses async/background execution, immediate job_id return, sha256-based cache-skip behavior, and directory expansion semantics. It omits auth/rate-limit details, but the core execution model is transparent.

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?

Front-loaded with the async job_id behavior, then structured notes for paths and follow-up polling. Efficient and scannable, with minimal filler.

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?

An output schema exists so return-value detail isn't needed, and the description covers the async flow, polling follow-up, caching, and input expansion. Sufficient for an agent to invoke and follow up correctly; only advanced concerns like auth or error modes are absent.

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 0%, so the description must compensate, and it does: it explains paths can be a single string or list and that directories expand into contained files, and it explains recursive behavior. This adds real meaning beyond the bare anyOf 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 opening sentence states a concrete verb and resource ('批量导入并分析二进制文件') and adds the key behavioral trait that it runs in the background in parallel and immediately returns a job_id. This clearly separates it from list_binaries and the query siblings, though it doesn't explicitly name a 'do not use if' alternative.

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?

Explicitly tells the agent what to do after the call: '返回后用 analysis_status(job_id) 轮询进度'. It also describes input handling (directories expand, recursive=true recurses) and that already-analyzed files are skipped as cached. Clear usage context, just no explicit when-not-use exclusion.

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