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Get Cycle

get_cycle
Read-onlyIdempotent

Get a single release cycle's support details for a product — release date, EOL, active-support end, latest patch, LTS, and any extended-support window. Use for a precise version question like "when does Python 3.9 lose support?". product is a slug from list_products; cycle is a version like "3.12", "20.04", "18". Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleYesRelease cycle / version, e.g. "3.12", "20.04", "18".
productYesProduct slug from list_products, e.g. "python", "ubuntu", "nodejs".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "cycle": "3.12",
      +    "product": "python"
      +  },
      +  {
      +    "cycle": "20.04",
      +    "product": "ubuntu"
      +  }
      +]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description adds value beyond annotations by stating the tool is 'Keyless' (no authentication) and listing the return fields (release date, EOL, etc.). Annotations already indicate read-only, idempotent, and non-destructive behavior, which is consistent and reinforced.

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?

The description is concise (4 lines), front-loaded with the main action, and every sentence adds value. No fluff or repetition.

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

Completeness5/5

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

For a simple retrieval tool with two well-documented parameters and no output schema, the description provides all necessary context: what it returns, how to use it, and key constraints (e.g., product slug source). Nothing essential is missing.

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?

The description explains that 'product' is a slug from list_products and provides example values for 'cycle'. Since schema coverage is 100%, the description adds practical guidance (e.g., where to get product slugs) that exceeds the schema's basic descriptions.

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 retrieves support details for a single release cycle, listing all fields (release date, EOL, etc.) and giving a concrete example. It distinguishes the tool's specific purpose from siblings like list_products, which only provide product slugs.

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 provides a usage scenario ('precise version question') and an example, which implies when to use. However, it does not explicitly state when not to use or mention alternative tools for broader queries, leaving room for improvement.

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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