list_free_llm_apisFree LLM / inference API tiers and credits (Gemini, Groq, OpenRouter, Mistral, Cohere, NVIDIA, …) with their limits and card rules.
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": "list_free_llm_apis"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description does add substantive content context that annotations do not: the returned records carry rate/usage limits and credit-card requirements, and the coverage spans multiple named vendors. It says nothing about result ordering, truncation, or whether this is a static curated list.
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?
One dense sentence with zero filler, and the most identifying information (free tiers, named vendors, limits and card rules) is front-loaded. It loses a point for being a sentence fragment whose ellipsis-style vendor list and shorthand 'card rules' are slightly cryptic.
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 listing tool with no output schema, the description does not explain the shape or size of the result set, the meaning of the limit parameter, or how it relates to the several overlapping sibling tools. An agent has enough to guess a call but not enough to choose this tool confidently.
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 single 'limit' parameter (1-50, optional) has 0% schema description coverage and is never mentioned in the description. The description therefore does not compensate at all for the unknown default value or the semantics of the cap.
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 names the specific resource (free LLM/inference API tiers and credits) and lists example providers plus the attributes returned (limits, card rules), so the content is identifiable. However it is a noun fragment with no verb, and it does not distinguish this catalog listing from siblings like search_free_ai_offers or compare_offers, which cover adjacent ground.
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?
There is no statement of when to use this tool rather than search_free_ai_offers, find_models, or compare_offers. The agent must infer that this is a browse/list endpoint versus a search or lookup endpoint based on the name alone.
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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