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SkillForge

SkillForge

by CatVinci-Studio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_optimization_history

View the evolution of a skill to understand past changes and feedback, preventing redundant edits.

Instructions

View the optimization history for a skill.

Use this to understand how a skill has evolved over time and what feedback drove each change. Helpful before making further edits to avoid reverting previous improvements.

Args: skill_name: The skill to check.

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_name": "<skill_name>"
    },
    "name": "get_optimization_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
skill_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states 'view' (implied read-only) but omits any details about permissions, data freshness, side effects, or output format. This is insufficient for a full transparency assessment.

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

Conciseness4/5

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

The description is concise, with a clear purpose statement and usage guideline in two sentences, followed by an args list. It is well-structured and front-loaded, though the args section is minimal.

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

Completeness3/5

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

Given the single parameter and existing output schema, the description covers the main purpose and usage context. However, it lacks behavioral details (e.g., permissions, output summary) and does not leverage the presence of an output schema to reduce the need for description.

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?

There is only one parameter, skill_name, and the description adds meaningful context: 'The skill to check.' This goes beyond the schema's minimal 'Skill Name' label, clarifying its role in the context of optimization history.

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

Purpose5/5

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

The description clearly states the tool's purpose: 'View the optimization history for a skill.' It elaborates on what that entails (how skill has evolved, feedback) and distinguishes it from siblings like request_skill_optimization and get_skill.

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

Usage Guidelines4/5

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

The description explicitly advises using this tool before making edits to avoid reverting improvements, providing clear when-to-use guidance. It does not list when not to use or name alternatives, but the context is helpful.

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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