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inferenceindexer-mcp

by io.github.DesMartin01

search_models

Search and list AI inference models with current pricing.

Args:
    query: Text search on model id/name (optional).
    tier: Filter by tier: frontier | standard | budget | micro | zdr | eu (optional).
    limit: Max results (1-100, default 25).
    sort: Sort key, e.g. 'blended' (price), 'sit' (SIT score) (optional).
Returns: models with input/output/blended $/M pricing, provider, tier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNo
tierNo
limitNo
queryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses the return payload (models with pricing, provider, tier), the accepted tier values, limit bounds, and example sort keys. It does not cover error behavior or data freshness, but it provides solid transparency for a read-only search tool.

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 compact and well-structured: a one-sentence summary followed by an Args block and a Returns line. Every sentence conveys necessary information without redundancy or padding.

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 search/list tool with four optional parameters and an output schema, the description is largely complete: it documents parameter semantics, return fields, tier options, and sorting. It does not explicitly address alternatives like get_model, and omits default sort behavior, but these are minor gaps given the overall clarity.

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 descriptions are 0% covered, so the description fully compensates by explaining all four parameters: query targets model id/name, tier lists valid filter values, limit gives range and default, sort gives examples with meanings. This is exactly the semantic detail an agent needs beyond the bare schema.

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: 'Search and list AI inference models with current pricing.' This clearly distinguishes search_models from siblings like get_model (single model lookup), list_providers, and compare_providers. The purpose is unambiguous even without a title.

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 establishes that this tool is for searching/listing models by text query, tier, limit, and sort, which implicitly distinguishes it from single-model or provider-level tools. It lacks an explicit statement of when not to use it or which sibling to prefer, but the intended context is clear.

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