LLM Provider Intelligence

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

No annotations are provided, so the description carries full burden. It states 'check' implying a read-only operation but does not explicitly confirm non-destructiveness, auth requirements, or what happens when provider is omitted. The behavioral disclosure is insufficient for a tool with no annotations.

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 a single sentence with no unnecessary words. It is front-loaded with the core action ('Check operational status and recent latency') and immediately scoped ('for one or all LLM providers'). Very concise.

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 has one optional parameter, no output schema, and no annotations, the description adequately covers the basic functionality. It does not explain return format or side effects, but for a simple status check this is arguably sufficient.

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% for the single parameter 'provider'. The description adds value beyond the schema by clarifying 'Omit for all', which is not in the schema description. This provides practical guidance on usage.

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 checks operational status and recent latency for LLM providers, and mentions 'one or all' which aligns with the optional parameter. It distinguishes from siblings like list_providers (listing) and recommend_llm (recommendation) by focusing on status/latency.

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 implies usage for checking status/latency but does not explicitly state when to use this tool versus alternatives like list_providers or recommend_llm. No exclusions or conditional guidance provided.

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?

No annotations are provided, so the description must disclose behavioral traits. It only states 'List', implying a read operation, but fails to mention any potential side effects, authentication requirements, rate limits, or whether the data is cached. The description is too minimal for full transparency.

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 concise with two sentences, front-loading the main action and resource, then mentioning filtering. Every sentence is informative without waste.

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 4 parameters and no output schema, the description covers the purpose and filters adequately but omits details like pagination, sorting, or the exact format of returned data (e.g., how 'capabilities, pricing, and agent scores' are structured). It is functional but not comprehensive.

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%, with each parameter well-described. The tool description merely says 'Filter by specific requirements', adding no semantic value beyond what the schema already provides. 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 action 'List' and the resource 'LLM providers', specifying included attributes (capabilities, pricing, agent scores). It distinguishes from sibling tools: 'check_provider_status' likely checks a single provider's status, and 'recommend_llm' gives recommendations, while this tool lists all providers with filtering.

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 mentions filtering, but does not provide explicit guidance on when to use this tool versus the siblings. No 'when not to use' or alternative recommendations are given, though the filtering capability implies use for exploration or comparison.

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?

No annotations are provided, so the description should disclose behavioral traits. It only mentions the output format but omits details like ranking methodology, side effects, dependencies, or error behavior.

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 concise sentences with no unnecessary words. Front-loaded with the main purpose and immediately provides the key output detail.

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 5 optional parameters and no output schema, the description provides a basic summary but lacks details on the expected response structure beyond 'top 3 ranked recommendations with reasoning.' Adequate but not fully comprehensive.

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?

With 100% schema coverage, the baseline is 3. The description does not add additional meaning beyond what the parameter descriptions already provide, so it meets but does not exceed expectations.

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 action ('Get'), resource ('best LLM provider'), and outcome ('top 3 ranked recommendations with reasoning'), distinguishing itself from sibling tools like check_provider_status and list_providers.

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 implies use for selecting an LLM but does not explicitly state when to use versus alternatives or provide exclusions. No guidance on when not to use.

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