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inferenceindexer-mcp

by io.github.DesMartin01

explain_model

Get everything about one model in a single call: the full picture.

Returns current pricing (input/output/blended, Cost/IQ, 24h/7d changes),
a price-history summary with trend, all provider endpoints, the cheapest
hand-verified endpoint with its native model id (for hot-swapping),
privacy flags (ZDR/EU availability), and the AA intelligence score.
Everything is as-of stamped.

Args:
    model_id: Canonical model id, e.g. 'anthropic/claude-sonnet-5'.
    history_days: Price-history window (default 30, max 365).
Returns: complete model profile with pricing, endpoints, privacy, quality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
history_daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral burden, and it delivers: it details current pricing changes, price-history trend, all endpoints, the cheapest hand-verified endpoint with native ID for hot-swapping, privacy flags, AA score, and as-of stamping. This goes well beyond a generic 'explains a model' statement.

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 front-loaded with the core purpose, followed by a compact bullet-like list of return contents and a short Args block. No unnecessary prose; the final return summary is brief and reinforces the payload.

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?

Given only two parameters and an available output schema, the description fully equips an agent to call the tool correctly: it explains result scope, model id format, and history window constraints, while the output schema handles detailed field structure.

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 coverage is 0%, but the Args section adds semantics: model_id is defined as canonical with a concrete example, and history_days is described as a price-history window with default 30 and max 365—far 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?

States a precise resource and action: 'Get everything about one model in a single call: the full picture.' The enumerated contents (pricing, endpoints, privacy, score) distinguish it from sibling tools that search, compare, or fetch history separately.

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 first sentence gives a clear usage context: use this when you need a complete one-model profile in a single call. It does not explicitly name alternatives or negative conditions, which keeps it from a 5.

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