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ankit-aglawe

tokencost-mcp-server

Compare Model Pricing

tokencost_compare_models
Read-onlyIdempotent

Compare pricing across multiple LLM models side by side to analyze input/output costs, context windows, and relative cost differences.

Instructions

Compare pricing across multiple LLM models side by side.

Args:

  • models (string[]): Array of model IDs or names to compare (2-10 models)

Returns: Side-by-side comparison table with input/output costs, context windows, and relative cost differences.

Examples:

  • ["gpt-5", "claude-sonnet-4.6"] → Compare OpenAI vs Anthropic pricing

  • ["gpt-5-mini", "gemini-3-flash", "claude-haiku-4.5"] → Compare budget models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsYesModel IDs or names to compare
Behavior4/5

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

Annotations already indicate read-only, non-destructive, and idempotent behavior, but the description adds useful context about the return format (side-by-side comparison table with specific columns) and model count constraints (2-10 models), which goes beyond what annotations provide.

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 well-structured with a clear purpose statement, organized sections for Args, Returns, and Examples, and every sentence adds value without redundancy. It efficiently communicates essential information in a compact format.

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?

Given the tool's moderate complexity, rich annotations, and 100% schema coverage, the description is mostly complete. It lacks an output schema but describes the return format adequately. A minor gap is no explicit mention of data sources or update frequency for pricing.

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?

Schema description coverage is 100%, so the schema already fully documents the 'models' parameter. The description adds minimal value beyond the schema by restating the parameter name and providing examples, but does not explain semantics like model ID formats or validation rules.

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 purpose with a specific verb ('compare') and resource ('pricing across multiple LLM models'), and distinguishes it from siblings like 'estimate_cost' or 'find_cheapest' by emphasizing side-by-side comparison rather than single calculations or optimization.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (comparing multiple models side-by-side), but does not explicitly state when not to use it or name alternatives among siblings. The examples imply usage for direct model comparisons rather than other pricing tasks.

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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