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LLMIntel Model Lifecycle

Recent lifecycle changes

recent_lifecycle_changes
Read-only

Lifecycle state changes across all tracked providers, newest first. Use this to answer 'what model deprecations happened recently?'.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "recent_lifecycle_changes"
  }
}

Input Schema

NameRequiredDescriptionDefault
providerNoRestrict to one provider.
sinceDaysNoLook back this many days. Defaults to 30.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / provider / enum
      Previous value: -[
      -  "openai",
      -  "anthropic",
      -  "azure",
      -  "bedrock",
      -  "google",
      -  "cohere",
      -  "mistral"
      -]New value: +[
      +  "openai",
      +  "anthropic",
      +  "azure",
      +  "bedrock",
      +  "google",
      +  "vertex",
      +  "mistral",
      +  "cohere"
      +]
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful context (result ordering 'newest first', breadth 'across all tracked providers') but says nothing about pagination, result limits, or volume of output, so it adds only moderate value beyond annotations.

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 tight sentences, zero filler, with the core scope statement front-loaded ahead of the usage hint. Every sentence earns its place.

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 a simple two-parameter, read-only list tool with no output schema, the description covers scope, ordering, and intent adequately, and annotations handle the safety profile. It is only slightly thin on what the returned entries contain, which is a minor gap given the absence of an output schema.

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 both the provider enum and sinceDays (default, bounds) are already documented in the schema. The description adds no parameter-level detail beyond that, making the baseline 3 appropriate.

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 resource ('lifecycle state changes') and scope ('across all tracked providers, newest first'), which is clearly more than a restatement of the name. It does not explicitly differentiate itself from siblings like list_retiring_models or check_model, so it stops at 4 rather than 5.

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?

Gives a concrete trigger phrase: "Use this to answer 'what model deprecations happened recently?'", which tells the agent the exact intent this tool serves. It offers no explicit when-not guidance or named alternatives among the siblings, so it falls short of 5.

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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