MK 完成度核验

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

No annotations exist, so the description carries the full burden. It does disclose meaningful behavior: a fresh fetch (not cached) and a return shape with per-task completePercent plus a separately-listed incomplete subset. However, it says nothing about read-only nature, auth/permission requirements, or how the learning tree is scoped.

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?

A single front-loaded sentence with no filler; the output contract is stated up front and the parenthetical clarifies the incomplete-item handling without padding.

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 annotations and no output schema, the description does a fair job of sketching the return value (completePercent per task plus a summary), but it leaves the sole required parameter and the tool's prerequisites unexplained, so an agent still cannot confidently construct the call.

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?

There is exactly one parameter (termId) with 0% schema description coverage, and the description never mentions it or explains what a term ID is, where it comes from, or whether it accepts string or number. The only parameter is therefore undocumented in both structured and natural-language form.

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 gives a concrete verb+resource: re-fetch the learning tree and emit each task point's completePercent plus a summary, with sub-1 items listed separately. This distinguishes it from mutation siblings like mk_complete_unit, but it never names a sibling or explicitly contrasts itself with the list_* tools, which is the main gap.

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?

Usage is only implied by '重新拉取' (re-fetch) – an agent can infer this is a post-completion verification call, but there is no explicit 'use this after X' or 'do not use for Y' statement, and no alternative tool is named.

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