LLM Provider Intelligence

recommend_llm

Get the best LLM provider for your specific agent task. Returns top 3 ranked recommendations with reasoning.

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": "recommend_llm"
  }
}

Input Schema

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)

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 12, 2026

TDQS

A3.8/5.0

Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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.

All LLM Provider Intelligence tools Inspect

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