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by io.github.InferIndex
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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, quantization, signals and training on prompts when published; the cheapest offer comes in detail. Pass detail: "full" for context, every published condition (data regions, sign-up) and reliability from official status pages on each offer.

Input Schema

NameRequiredDescriptionDefault
sortNoSort order: default blended (3:1 mix), or estimated_cost (the cost of your load) when prompt_tokens or output_tokens is given; estimated_cost needs one of them
limitNoNumber of offers to return, cheapest included (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.
detailNocompact (default): the winner in detail and one short line per other offer (provider, prices, signals, training on prompts when published). full: every field of every offer (all published conditions, reliability), much longer
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, invitation or country restriction)
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. Changed1 schema field changed
    • changedInput schema / properties / sort / description
      Previous value: -"Sort order (default blended); estimated_cost needs prompt_tokens or output_tokens"New value: +"Sort order: default blended (3:1 mix), or estimated_cost (the cost of your load) when prompt_tokens or output_tokens is given; estimated_cost needs one of them"
  2. Changed2 schema fields changed
    • addedInput schema / properties / detail
      Added value: +{
+  "description": "compact (default): the winner in detail and one short line per other offer (provider, prices, signals, training on prompts when published). full: every field of every offer (all published conditions, reliability), much longer",
+  "enum": [
+    "compact",
+    "full"
+  ],
+  "type": "string"
+}
    • changedInput schema / properties / limit / description
      Previous value: -"Number of offers to return (default 10, max 50)"New value: +"Number of offers to return, cheapest included (default 10, max 50)"
  3. Changed1 schema field changed
    • changedInput schema / properties / no_waitlist / description
      Previous value: -"Only providers with open sign-up (no waitlist or invitation)"New value: +"Only providers with open sign-up (no waitlist, invitation or country restriction)"
  4. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description adds real behavioral value beyond that: the default limit of 10, cheapest-first ordering, the fact that only the cheapest offer is expanded, and that detail:'full' surfaces published conditions and status-page reliability.

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?

Two sentences, front-loaded with the core behavior (offers, priming, ordering, default count) and then the detail escalation. Dense but every clause carries information; the only mild cost is the long parenthetical about what 'full' returns.

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 13-parameter read tool with no output schema, the description does the heavy lifting on return shape: one line per provider/source, cheapest in detail, and what 'full' expands to. It omits coverage of filters like region, strict, no_training and no_waitlist, but those are fully documented in the schema.

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 coverage is 100%, so all 13 parameters are documented in the schema and the baseline is 3. The description restates the default ordering and detail modes (which the schema already explains) but adds little syntactic or semantic detail beyond it, so it neither compensates nor materially enriches.

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?

States a specific verb and resource: current offers for one model, one line per provider and source, cheapest first. An agent can tell this is a cross-provider price/offer comparison rather than a single-price lookup. However, it never names or contrasts the overlapping sibling 'cheapest', so the differentiation is left implicit.

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

Usage Guidelines2/5

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

The description never states when to use this tool versus 'cheapest', 'estimate_cost', 'self_host_or_api' or 'price_history'. The only routing hint ('use search_models when unsure') lives in the model param schema, not in the description, and it addresses input resolution rather than tool selection.

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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