Get full detail + current pricing for one model by its id.
Args:
model_id: Canonical model id, e.g. 'openai/gpt-5.6' or 'anthropic/claude-sonnet-5'.
Returns: pricing, tier, SIT score, quality-adjusted price (Cost/IQ).
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It communicates that the operation is a read-only-style get for a single model and lists the returned fields, but it does not mention error behavior, data freshness, authentication needs, or rate limits. This is baseline transparency but not richer behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear, one-line purpose and is followed by a compact Args/Returns breakdown. Every sentence earns its place; there is no filler, redundancy, or boilerplate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with an output schema present, the description covers the operation, input format, and key return values. It could add a note about using search_models when the id is unknown or what happens on an invalid id, but those are marginal given the tool's simplicity and existing output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema offers zero description for model_id, but the description's Args section fully compensates by explaining the parameter's meaning and giving concrete, canonical examples such as 'openai/gpt-5.6' and 'anthropic/claude-sonnet-5'. This completely eliminates any ambiguity about the parameter format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('full detail + current pricing for one model by its id'), which clearly distinguishes it from sibling tools like search_models (search by criteria) and get_model_history (history over time). The phrase 'by its id' makes the singular, lookup nature immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the appropriate context: use this when you already know a canonical model_id and want detailed information plus pricing. However, it does not explicitly state when not to use it or point to alternatives such as search_models for unknown IDs, stopping at implied guidance rather than explicit routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource/action, but get_model and compare_providers both surface pricing for a single model, and search_models could overlap with get_model for direct lookups. The descriptions are clear enough to resolve the ambiguity in practice.
All tools follow a consistent verb_noun snake_case pattern: get_* for details and history, list_* for enumeration, search_* for filtering, and compare_* for cross-provider comparison. No mixed naming conventions or vague verbs.
8 tools is well-scoped for an inference pricing/index analytics server: model detail, model history, provider listing, provider detail, cross-provider comparison, and composite index current/history. Each tool has a clear role and none feel redundant.
The read-oriented domain is well covered: models have search/detail/history, providers have list/detail/comparison, and the composite index has current and historical views. Minor gaps such as provider-level price history or direct multi-model side-by-side comparison are not essential and can be worked around with existing tools.