LLM provider intelligence for AI agents. Get ranked recommendations for your task type, compare pricing and capabilities. Tools: recommend_llm, list_providers, check_provider_status.
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Average 3.8/5 across 3 of 3 tools scored.
Each tool targets a distinct function: status monitoring, provider listing, and recommendation. No overlap in purpose or output.
All tools follow a verb_noun pattern (check_provider_status, list_providers, recommend_llm), consistent and predictable.
3 tools is a focused and appropriate set for the server's purpose of LLM provider intelligence, not too sparse nor excessive.
The set covers the core workflow: viewing providers, checking their operational status, and getting recommendations with reasoning. No obvious gaps.
check_provider_statusAInspectCheck operational status and recent latency for one or all LLM providers.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | Specific provider slug (e.g. openai-gpt4o). Omit for all. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing safety and behavior. It indicates a non-destructive status check, but does not mention data freshness, return format, rate limits, or specific status values. Adequate but not rich.
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, front-loaded sentence that states exactly what the tool does without wasted words or repetition of schema fields.
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?
The tool is simple with one optional parameter and no output schema. The description covers purpose and scope adequately, but could be slightly more complete by stating the output format (e.g., status and latency values for each provider).
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%, so the schema already explains the provider parameter with an example and the 'omit for all' behavior. The tool description adds little beyond this, so baseline 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 uses a specific verb ('Check') and resource ('operational status and recent latency for one or all LLM providers'), clearly distinguishing it from siblings like list_providers and recommend_llm.
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 scope by mentioning 'one or all' providers, but it does not explicitly state when to use this tool over siblings list_providers or recommend_llm. No exclusions or alternative tool names are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_providersAInspectList LLM providers with their capabilities, pricing, and agent scores. Filter by specific requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| best_for | No | Filter by use case: tool_calling, reasoning, cost, speed, long_context, rag | |
| max_price | No | Max input price per million tokens | |
| x402_native | No | Only show x402-native providers | |
| tool_calling | No | Only show providers with tool calling |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It communicates a read-like operation ('List') and what data is returned, but does not disclose any additional behavioral traits such as pagination, rate limits, or authentication requirements.
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, clear sentence followed by a concise filter note. Every word earns its place with no redundancy.
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?
The tool is simple with four optional, well-documented parameters and no output schema. The description adequately covers the return content (capabilities, pricing, agent scores) and the filtering capability. Sibling tools exist but their absence from the description does not create a critical 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?
The schema provides 100% coverage with detailed descriptions for all four parameters. The description only generically mentions filtering and adds no new parameter-specific meaning beyond the schema.
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 uses the specific verb 'List' and clearly identifies the resource 'LLM providers' along with the included attributes (capabilities, pricing, agent scores). This distinguishes it from siblings 'check_provider_status' and 'recommend_llm'.
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 through 'Filter by specific requirements' but does not explicitly state when to use this tool versus the alternatives. No exclusions or named alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_llmAInspectGet the best LLM provider for your specific agent task. Returns top 3 ranked recommendations with reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | Task type: tool_calling, reasoning, rag, summarization, high_volume, low_latency, general | general |
| priority | No | Optimize for: balanced, cost, speed, quality | balanced |
| min_context | No | Minimum context window needed in tokens | |
| require_tool_calling | No | Only return providers with tool calling support | |
| max_price_per_million | No | Maximum input price per million tokens (USD) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool returns top 3 recommendations with reasoning, which gives some insight into the output. However, it does not mention how recommendations are generated, whether they are real-time or static, or any limitations. With no annotations, more detail 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 concise, consisting of two short sentences that immediately convey the purpose and the output format. It is well-structured and free of unnecessary content.
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?
The description adequately covers the core behavior and output shape, and the schema fully documents parameters, so the tool is understandable without an output schema. It could be slightly more detailed about how recommendations are computed, but overall it is 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?
The schema provides full descriptions for all 5 parameters, including task types, priority options, and filter criteria, so the description does not need to add parameter-specific details. The description adds no additional meaning beyond the schema.
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's function: it gets the best LLM provider for a task and returns top 3 ranked recommendations with reasoning. This distinguishes it from siblings like check_provider_status and list_providers, which focus on status and listing respectively.
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 the tool is for choosing an LLM provider for an agent task ('Get the best LLM provider for your specific agent task'), but it does not explicitly state when to use it over the sibling tools or when not to use it. No exclusions or alternative suggestions are given.
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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