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

With no annotations, the description carries the full burden and does solid work: it discloses a 5-minute per-URL cache, that redirects are marked with the final URL, that oversized bodies are truncated and flagged, and that auto mode falls back to a headless browser. It omits failure modes entirely — timeouts, non-HTML content, blocked/paywalled pages, or whether the browser fallback has a cost/latency penalty worth avoiding.

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?

Summary sentence is front-loaded and high-signal; the Args block is compact and each entry earns its place. Minor redundancy in restating defaults that also appear in the schema, but with 0% schema coverage that restatement is functional rather than wasteful.

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 values need not be re-explained; the description still adds the useful note that redirects append the final URL and that truncation is marked. For a 3-param fetch tool the remaining gap is error/edge behavior, which is the main thing an agent would still be guessing at.

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 0%, so the description must compensate and it does: all three parameters are explained with types, defaults, and behavior (max_chars default 20000 plus truncation-and-marking), and render_mode's three accepted string values are enumerated with tradeoffs despite the schema having no enum. This adds meaning the schema does not provide.

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 names a specific verb+resource pair (抓取网页正文, convert to Markdown) and adds differentiating scope: automatic stripping of navigation/ads and Chinese encoding handling. Against siblings search_web and search_github, which locate pages rather than return their content, the tool's role is unmistakable from the description alone.

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?

render_mode guidance is explicit about when to pick each mode (browser for known JS pages to avoid a wasted HTTP round-trip, request for max speed), and the cache rule tells the agent repeat fetches within 5 minutes are cheap. It stops short of exclusions — nothing says when not to use this tool or what happens on non-HTML/auth-gated URLs.

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?

No annotations exist, so the description carries the burden. It discloses the result cap range (1–20), the default (8), the sort vocabulary, and the supported qualifier set — real behavioral detail. It says nothing about GitHub API authentication/token requirements or unauthenticated rate limits, which are the main failure modes for this tool, and it doesn't characterize what a result entry contains beyond 'returns N results'.

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?

Purpose is front-loaded in the first line, followed by examples and then a per-argument block — good scan order. The qualifier list partially duplicates what the examples already demonstrate, so there is mild redundancy, but every line is actionable.

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 explanation is not needed, and the query grammar — the hardest part of this tool — is covered thoroughly. The remaining gap is operational: no mention of auth/token or rate limits for the official API, which matters for an agent that may call it repeatedly.

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% (titles only), yet the description fully compensates: `query` is documented with combinable qualifiers (language:, stars:>N, topic:, pushed:>, license:, in:name,description) plus three concrete examples; `max_results` documents default 8 and the 1–20 bound; `sort` enumerates the four valid string values even though the schema declares none.

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?

States a specific verb+resource (search GitHub repositories) and adds scope via the official API plus the 'find open-source projects/cases/libs' gloss, which separates it from the generic search_web sibling in spirit. It never names the siblings explicitly, so the differentiation is implied rather than stated.

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 parenthetical '(official API, use this for finding open-source projects/cases/wheels)' gives a clear use context, and the three worked query examples show realistic invocation patterns. It lacks an explicit 'when not to use this / use search_web instead' clause, so it stops short of full routing guidance.

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, the description carries the full behavioral burden. It does disclose the latency tradeoff (default fetch_top 0 keeps latency low), that content fetching is concurrent plain HTTP with ~800 characters per snippet, and the numeric bounds (1-20 results, 0-3 fetches). It says nothing about rate limits, failure modes, or throttling, which matters for a scraping-backed search 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?

Front-loaded one-line purpose followed by a compact parameter block; each parameter note earns its place with a default or a use case. Slightly verbose but no filler sentences.

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 values needn't be spelled out, and all five parameters are fully documented. The only gap is failure/rate-limit behavior, which is forgivable given the output schema and complete parameter coverage.

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 0%, so the description must compensate and it does: every one of the five parameters gets meaning, valid values, and defaults — region enumerations (wt-wt/cn-zh/us-en), timelimit codes (d/w/m/y), result caps, and the 0-3 fetch_top range. It explains far more than the bare 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 first line names a specific verb (联网搜索 / web search), the engine (DuckDuckGo), and the return shape (a result list with links). It implicitly separates itself from search_github and fetch_url_content by being a general web search, but never names those siblings explicitly.

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?

It gives real selection guidance: use timelimit "w"/"d" for news queries, and set fetch_top to 2-3 when you want direct answers instead of fetching results one by one — which routes the agent away from the sibling fetch_url_content. It lacks explicit when-not-to-use or a direct comparison to search_github.

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