LLMIntel Model Lifecycle

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

Description adds value beyond annotations (readOnlyHint, openWorldHint) by clarifying the tool returns status and migration recommendation. No contradiction. Could mention that it does not modify any state, but annotations already cover safety.

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

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

Two sentences, no fluff. First sentence clearly states purpose and outputs. Second sentence provides actionable usage instruction. Every word earns its place.

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

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

Given one parameter and no output schema, the description adequately explains the tool's purpose and when to use it. It could mention the structure of the return value, but the tool is straightforward and the description suffices for an agent to invoke it correctly.

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

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

Schema coverage is 100% (single parameter with description). Description adds concrete examples ('gpt-4o', 'claude-sonnet-4-5-20250929') and reinforces that it must be the exact code string. This exceeds the schema's basic description.

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

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

Title 'Check a model's lifecycle status' and description specify verb ('check'), resource ('model lifecycle status'), and scope ('safe, deprecated, retired, migration'). Clearly distinguishes from sibling tools like search_models or suggest_replacement by focusing on exact model IDs and lifecycle status.

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

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

Explicitly states 'Call this before writing or changing any hardcoded model id.' Provides usage context (exact string from code). Does not explicitly mention when not to use, but sibling tools offer alternatives for broader searches or replacements.

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

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

Annotations already declare readOnlyHint and openWorldHint, so description's behavioral disclosure is less critical. The description adds ordering (soonest first) but lacks details like pagination or result limits, which would be helpful.

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

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

The description is short (two sentences) and front-loaded with purpose. It efficiently conveys the tool's function and use case without extraneous text.

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

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

With no output schema, the description adequately hints at what is returned (list of retiring models, ordered). Given simple parameters and clear purpose, the description completes the context well.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description does not add additional meaning beyond what is in the schema properties.

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

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

The description clearly states the tool lists models retiring soon, with ordering (soonest first) and use cases (audit codebase, plan migrations). It distinguishes from siblings like search_models or recent_lifecycle_changes by focusing on imminent retirements.

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

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

The description explicitly says when to use it: 'audit a codebase or plan migration work.' It implies not to use for general model search or recent changes, but does not list explicit exclusions or alternatives.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds that results are ordered newest first and cover all tracked providers, which provides useful behavioral context beyond annotations without contradiction.

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

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

The description is extremely concise: one sentence stating the tool's function and ordering, plus a usage hint. No unnecessary words, front-loaded with key information.

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

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

For a simple list tool with two well-documented parameters and readOnlyHint/openWorldHint, the description covers purpose and ordering. However, it does not describe the output format (e.g., fields like provider, model, date, change type), which could be a minor gap.

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

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

Schema description coverage is 100%. Both parameters ('provider' and 'sinceDays') are described in the schema. The description does not add additional meaning beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool lists lifecycle state changes across all providers, ordered newest first, and provides an example query ('what model deprecations happened recently?'). This distinguishes it from siblings like list_retiring_models (retiring only) and check_model (single model).

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

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

The description includes a specific usage example ('Use this to answer...'), giving clear context. However, it does not explicitly mention when not to use the tool or suggest alternatives among siblings, such as list_retiring_models for retirement-specific queries.

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

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

Annotations already declare readOnlyHint and openWorldHint, so description carries less burden. It adds no further behavioral details (e.g., pagination, completeness). Adequate but not enhanced.

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

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

Two sentences, front-loaded with action, no waste. Very concise and well-structured.

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

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

Given the tool's simplicity, annotations, and full schema coverage, the description is complete. It addresses the core purpose and usage without needing more detail.

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

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

Schema coverage is 100% with descriptions for all 3 parameters. Description merely restates the filter options (provider and lifecycle state) already documented in schema. No additional value beyond baseline.

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

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

The description clearly states it lists tracked models with optional filters by provider and lifecycle state. The verb 'list' and resource 'tracked models' are specific. It distinguishes from siblings like check_model (details) and list_retiring_models (specific subset).

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

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

Explicitly advises use to discover what's available from a provider. Does not exclude when not to use it, but the context is clear enough given sibling tool names.

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

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

The description goes beyond annotations by explaining the fallback behavior (active models from same provider when no official replacement). No contradictions with readOnlyHint or openWorldHint.

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

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

Single sentence with no redundancy. All information is essential and front-loaded.

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

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

With no output schema, the description provides adequate coverage of behavior for a simple tool, though it could specify return format (e.g., list of model names).

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

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

Schema coverage is 100% and the description adds context about the input type (deprecated/retiring models), which clarifies valid inputs beyond the schema's 'model id or alias'.

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

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

The description clearly states the tool returns recommended replacements for deprecated models, with a specific verb ('return') and resource. It is distinct from siblings like search_models or list_retiring_models.

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

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

The description explicitly says when to use ('given a deprecated or retiring model') but does not provide when-not-to-use or mention alternative tools for related tasks.

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