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Check Model Lifecycle

check_model_lifecycle

Check the lifecycle status of a Python model at a specified date without live research. Use this to validate or plan migrations based on reviewed facts.

Instructions

Interpret reviewed lifecycle facts at a fixed date without live research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_of_dateNo
identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool does not perform live research and works from reviewed facts, which implies a read-only, offline behavior. However, it does not mention side effects, staleness, or failure behavior, leaving some transparency gaps.

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 a single focused sentence with no redundancy or filler. It is appropriately compact for a simple tool, though the terseness leaves some semantic gaps.

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

Completeness2/5

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

Despite having an output schema, the tool lacks annotations and has zero parameter documentation in the schema. The description does not define 'lifecycle facts,' explain the identifier input, or give enough selection criteria among the many sibling tools. It is not complete enough for an agent to confidently invoke it.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for parameter meaning. It only hints at 'a fixed date' (matching as_of_date) but never explains the required 'identifier' or the format/meaning of the date. This is insufficient for correct invocation.

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 ('Interpret') and resource ('reviewed lifecycle facts'), and adds a fixed-date scope plus 'without live research.' It is clear enough to distinguish the tool from live-research siblings, though it does not explicitly name alternatives.

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 phrase 'without live research' provides clear contextual guidance: use this tool when you need reviewed lifecycle facts at a fixed date rather than live research. It does not explicitly list alternatives or when-not-to-use conditions, but the context is reasonably clear.

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