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InferIndex

Compare providers for a model

compare_providers
Read-onlyIdempotent

Current offers for one model, one line per provider and source (direct or via an aggregator), cheapest first (10 by default), with price, context, quantization, published conditions (training on prompts, data regions, sign-up) and reliability from official status pages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort order (default blended); estimated_cost needs prompt_tokens or output_tokens
limitNoNumber of offers to return (default 10, max 50)
modelYesModel id or name, e.g. 'deepseek-v3.2', 'deepseek/deepseek-v4-pro', 'gpt-5.6-luna'. Use search_models when unsure.
regionNoOnly providers that process data in this region: eu, us, …
strictNoExclude offers whose provider does not publish the filtered information (by default they are kept and flagged)
no_trainingNoOnly providers whose published terms say they do not train on your prompts
no_waitlistNoOnly providers with open sign-up (no waitlist or invitation)
cached_ratioNoShare of input tokens served from the provider's prompt cache (0 to 1)
include_tiersNoAlso include lower-priority service tiers, comma-separated: flex, batch (hidden by default)
output_tokensNoOutput tokens per request, for the estimated cost
prompt_tokensNoInput tokens per request, for the estimated cost
requests_per_dayNoRequests per day, to also get an estimated monthly cost

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description only needs to add behavioral context. It does this well: cheapest-first ordering, 10-offer default, direct/aggregator sources, included fields, and reliability sourced from official status pages.

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?

One dense sentence front-loads the core behavior and output contract without repeating annotations or schema. The long enumerations could be easier to scan as bullets, but every piece of information earns its place.

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?

There is no output schema, so the description wisely provides a compact return contract: one line per provider/source with price, context, quantization, conditions, and reliability. Combined with fully described parameters and non-destructive annotations, this is sufficient for an agent to invoke the tool correctly.

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 baseline is 3. The description adds some output-level context (default 10, sorted by price, fields like training conditions and reliability), but it does not add meaning to the individual parameters beyond what the schema already documents.

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 states a specific verb and resource: it compares current offers for one model, one line per provider and source. It also distinguishes itself from siblings like price_history by emphasizing 'current offers' and from cheapest by showing all providers/sources, not just the cheapest.

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?

It clearly scopes usage to fetching current offers for a single model, with explicit output shape and ordering. The model parameter adds a concrete routing hint ('Use search_models when unsure'), though it does not explicitly contrast this tool with cheapest, estimate_cost, or price_history.

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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