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inferenceindexer-mcp

by io.github.DesMartin01

get_model_history

Get HISTORICAL price data / trends for one model.

This is InferenceIndexer's differentiator: aggregators like OpenRouter
expose only current price; this returns the price over time (input,
output, blended $/M), enabling trend analysis.

Args:
    model_id: Canonical model id, e.g. 'openai/gpt-5.6'.
    days: History window in days (1-365, default 30; plan-dependent).
Returns: historical price series for the model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
model_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that this tool returns time-series price data across input, output, and blended $/M, and notes that the 'days' window is plan-dependent. This goes well beyond the tool name and schema, though it could have mentioned error cases or granularity, which are not critical here.

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 concise and well-structured: a one-sentence purpose, a short differentiator, an Args list, and a Returns line. Every section earns its place, and the purpose is front-loaded. There is no redundant filler.

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

Completeness5/5

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

For a two-parameter read-only historical data tool, the description is complete: it covers model_id format, the days window constraints, the return nature, and the underlying use case. The output schema can handle the detailed return structure, so the description does not need to repeat field-level output details.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate, and it does. It defines model_id with a concrete canonical format example, and days with range (1-365), default (30), and plan-dependency. Both parameters are meaningfully explained beyond the bare schema types.

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 opens with a specific verb and resource: 'Get HISTORICAL price data / trends for one model.' It clearly distinguishes this tool from current-price lookups and composite history tools by emphasizing 'one model' and the time-series nature of the data. This makes it easy for an agent to separate it from siblings like get_model or get_composite_history.

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 clearly signals the intended use case: when historical price trends are needed rather than the current price. It explicitly contrasts with aggregators like OpenRouter that only expose current price, helping the agent decide when to call this tool. It does not explicitly name sibling alternatives, but the context is clear enough to infer the right usage.

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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TDQS

A4.4/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources