AgentIntel
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
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.
Available Tools
3 toolscheck_provider_statusAInspect
Check 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description implies read-only, but does not disclose permissions, rate limits, or side effects. Acceptable for a simple status check.
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, no wasted words, effectively communicates purpose and parameter scope.
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?
Adequately describes a simple tool with one optional parameter and no output schema. Could mention output format or latency range, but not critical.
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 already includes a description. The tool description adds minimal value beyond restating 'one or all'.
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 checks operational status and recent latency for LLM providers, distinguishing it from siblings 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?
Implies usage for checking status/latency vs listing or recommending. Provides example of provider slug, but lacks explicit when-not-to or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_providersAInspect
List 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description fails to disclose pagination, result limits, data freshness, or whether it's a read-only operation. Minimal behavioral context.
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, front-loaded with purpose. No extraneous 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?
Covers basic purpose and filtering, but missing behavioral details and example usage. With no annotations or output schema, completeness is average.
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 covers all 4 parameters with descriptions (100% coverage). Description adds little beyond 'filter by specific requirements'.
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?
Clearly states it lists LLM providers with specific attributes (capabilities, pricing, agent scores) and supports filtering. Distinguishes from siblings like '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?
Implies use when listing/filtering providers, but lacks explicit guidance on when to prefer this over 'recommend_llm' or 'check_provider_status'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_llmAInspect
Get 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) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It hints at non-destructive behavior (recommendations) and mentions the output, but lacks details on side effects, authentication, or error handling.
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 wasted words. Essential information is 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?
The description covers purpose and output format. Given 5 parameters and no output schema, it adequately explains the return value. Minor gaps in error scenarios but acceptable.
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 parameters are already well-described. The description adds no extra meaning beyond what the schema provides, meeting the 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 the tool's verb ('Get'), resource ('best LLM provider'), and output ('top 3 ranked recommendations with reasoning'). It distinguishes from siblings like check_provider_status and list_providers by focusing on recommendations.
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 'specific agent task' but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance.
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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