llm-price-mcp

cheapest

Rank LLM API models by combined cost per 1M input and output tokens, then filter results by minimum context window and provider keyword.

Instructions

Return the cheapest models, ranked by cost of 1M input + 1M output tokens.

Args: min_context: Only models with at least this context window (tokens). keyword: Optional provider/keyword filter, e.g. "qwen". limit: How many models to return (default 10). include_free: Include $0 models (often rate-limited); default False.

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": "cheapest"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`keyword`	No
`min_context`	No
`include_free`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 3, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description usefully discloses the ranking workload, the default exclusion of $0 models, and the warning that free models are often rate-limited. “Return” suggests a read-only lookup, but safety, prerequisites, and pricing source or freshness are not addressed.

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 purpose and ranking metric are front-loaded, followed by a compact argument list. Every line contributes operational information, with no filler or unnecessary repetition.

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?

For a simple tool with four optional parameters and an output schema, this covers nearly everything needed to invoke it correctly without needing to explain return fields. Explicit sibling differentiation and slightly more pricing-scope context would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the Args section documents all four parameters. It adds token units for min_context, provider/keyword interpretation and an example for keyword, the limit default, and include_free's default behavior and practical caveat.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening line specifies the operation and resource: returning models ranked by the combined cost of 1M input and 1M output tokens. This makes the ranking basis clear, but it does not explicitly differentiate the tool from siblings such as compare_cost or list_models.

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?

Finding low-cost models is the implied use case, and the documented filters help tailor that search. There is no explicit guidance on when to choose this tool over compare_cost for selected-model comparisons or list_models for general discovery.

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

Deploy Server