apipriceindex-mcp

Official

by apipriceindex

Overview Schema Source Related Servers Discussions Admin

Python

Remote

compare_models

Compare two to four models side by side.

Instructions

Compare two to four models side by side.

For each: lowest input, output and cached-input price with the provider that gives it, number of providers, fastest provider (OpenRouter median), context window, license, Epoch Capabilities Index score; best names the winner of each row. Give monthly volumes (millions of tokens) to add the monthly cost at each model's cheapest provider.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "models": "<models>"
    },
    "name": "compare_models"
  }
}

Input Schema

Name	Required	Description	Default
`models`	Yes
`cache_pct`	No
`input_mtok`	No
`output_mtok`	No

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.3.0Oct 28, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses the output structure, the `best` winner logic, and the conditional monthly-cost calculation when volumes are supplied. It omits important behavioral details such as what happens outside the two-to-four-model limit, provider availability failures, or whether the operation has side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core action and output fields. The long middle sentence earns its place by enumerating the comparison dimensions, though the line break and semicolon-heavy structure are slightly less scannable than ideal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description must provide enough context for correct invocation. It explains return values reasonably well, but with 0% parameter schema coverage it leaves `cache_pct` and exact volume semantics unexplained, and it omits error or boundary behavior for the two-to-four model constraint.

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

Parameters2/5

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

Schema description coverage is 0% for four parameters, so the description must compensate. It implies that monthly volumes map to `input_mtok` and `output_mtok` and mentions millions of tokens, but it never names those parameters or explains `cache_pct` at all. The cache percentage parameter remains completely opaque.

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 description states a specific verb and resource: compare two to four models side by side. It also enumerates the comparison dimensions and the `best` row-winner behavior, making the tool's purpose clear. It does not explicitly distinguish itself from siblings such as `estimate_cost` or `get_price`, so it falls short of a 5.

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?

Usage is implied through the optional monthly volume inputs, which add monthly cost estimates at each model's cheapest provider. However, there is no explicit guidance on when to use this tool versus `estimate_cost`, `get_price`, or `search_models`, nor any exclusions or prerequisites.

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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