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Glama

沉淀一条优势(强制四步链条)

strength_save

Save an evidence-backed strength only after validating the evidence → behavior → ability → job-signal chain; reject incomplete, vague, or personality-label entries.

Instructions

保存一条已挖掘的优势。必须完整填写 证据 → 行为 → 能力 → 岗位信号 四步链条,缺任意一步、或证据过于空泛、或把性格标签当优势,都会被拒绝并说明原因。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes优势标签(能力名),3-8 字,如「跨团队推动落地」
abilityYes③ 能力:从行为抽象出的可迁移能力,而不是任务本身
behaviorYes② 行为:我具体做了什么(动词 + 对象 + 情境 + 结果)
categoryYes能力分类
evidenceYes① 证据:客观事实(项目/数据/作品/第三方评价/时间),不得虚构
jobSignalYes④ 岗位信号:该能力对目标岗位的价值,尽量对齐 JD 用词
confidenceYes证据强度:high=有作品/数据/第三方评价;medium=有结果缺外部证据;low=仅自我评价
targetRolesNo适用岗位方向
missingProofNo还缺什么证据;非空表示暂不得写入正式投递材料
elevatorPitchNo可直接使用的表达话术(简历 bullet / 面试口述)
classificationYes分级:fact=有据可查 / inference=推理 / assumption=假设

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses three rejection conditions and that failures come with an explanation, but says nothing about persistence semantics: duplicate titles, overwrite vs append behavior, or what a successful save returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, purpose front-loaded, constraint second. No filler, no restatement of the tool name, and the enforcement rules are packed into a single readable clause.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter write tool with full schema descriptions and no output schema, the description covers the core chain and failure modes adequately. The gap is success-side semantics (duplicate handling, what is persisted) and no mention of the optional targetRoles/missingProof/elevatorPitch fields, though the schema documents those.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by framing evidence → behavior → ability → jobSignal as a mandatory ordered chain and defining what makes evidence unacceptable ('过于空泛') or a title invalid (personality label). That goes beyond the per-field schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: saving one strength ('保存一条已挖掘的优势'), and the word '已挖掘' hints that it consumes output from strength_mine rather than producing it. It never names the sibling explicitly, so the differentiation from strength_mine/strength_list/strength_delete is left partly to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies the strength must already exist before saving and enumerates the conditions under which the write will be refused (incomplete chain, vague evidence, personality label as strength). However, it never states the upstream step ('run strength_mine first') or names any alternative sibling, so the when-to-use routing is only implied.

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