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skill-maintenance-mcp

skill-maintenance-mcp

by mo9652962-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Skill Log Decision

skill_log_decision

Appends a four-field decision record (diagnosis, revision, evidence, result) to a skill's decision log, capturing why changes were made to avoid repeated mistakes.

Instructions

向技能的 references/decision-log.md 追加决策记录（四字段: 诊断/修订/证据/结果——记「为什么改」, 未来 agent 不重新踩坑）。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "skill_path": "<skill_path>",
      "title": "<title>",
      "diagnosis": "<diagnosis>",
      "revision": "<revision>",
      "evidence": "<evidence>",
      "result": "<result>"
    },
    "name": "skill_log_decision"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYes一句话标题（自动加日期前缀）
resultYes结果——接受/拒绝 + 原因
evidenceYes证据——评估/实测结果
revisionYes修订——改了哪里
diagnosisYes诊断——技能在什么场景失效
skill_pathYes技能目录绝对路径

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
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. 'Append' usefully signals an additive, non-destructive write to a specific file path, which is meaningful context. It does not state whether the file is created if absent, whether entries are deduplicated on repeat calls, or what permissions are required for a mutation tool.

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?

A single dense sentence with the verb and target file front-loaded, followed by a parenthetical that explains both the record structure and the reason the tool exists. Nothing is wasted and no padding is present.

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?

An output schema exists, so return values need not be explained, and the description covers purpose, target artifact, and record shape. Remaining gaps are edge cases such as missing-file creation and duplicate handling, which matter for a write tool with no annotations but are secondary.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all six parameters, including the four content fields. The description restates the four field names (diagnosis/revision/evidence/result) but adds no format, length, or content guidance beyond what the schema descriptions provide. Baseline 3 applies.

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?

The description gives a specific verb+resource: append a decision record to the skill's references/decision-log.md, and names the exact artifact and the four fields it contains. It is clearly distinguishable from the read-side sibling skill_read_decisions, though it never explicitly names that counterpart.

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?

It conveys the underlying rationale (record 'why a change was made' so future agents don't repeat the mistake), which implies the appropriate moment to use it. However, there is no explicit when/when-not guidance and no reference to the read sibling skill_read_decisions, so routing relies on inference.

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

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